{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "00_Keras_RNN_predictions_playground.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_wxmLPc2YysH",
        "colab_type": "text"
      },
      "source": [
        "# An RNN for short-term predictions\n",
        "This model will try to predict the next value in a short sequence based on historical data. This can be used for example to forecast demand based on a couple of weeks of sales data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uk3AMTcYysJ",
        "colab_type": "code",
        "outputId": "398ef7ce-36e9-43da-c380-08bee5462ee4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# using Tensorflow 2\n",
        "%tensorflow_version 2.x\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import tensorflow as tf\n",
        "print(\"Tensorflow version: \" + tf.__version__)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensorflow version: 2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sk3oZyKsZLRf",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Display utilities [RUN ME]\n",
        "\n",
        "from enum import IntEnum\n",
        "import numpy as np\n",
        "\n",
        "class Waveforms(IntEnum):\n",
        "    SINE1 = 0\n",
        "    SINE2 = 1\n",
        "    SINE3 = 2\n",
        "    SINE4 = 3\n",
        "\n",
        "def create_time_series(waveform, datalen):\n",
        "    # Generates a sequence of length datalen\n",
        "    # There are three available waveforms in the Waveforms enum\n",
        "    # good waveforms\n",
        "    frequencies = [(0.2, 0.15), (0.35, 0.3), (0.6, 0.55), (0.4, 0.25)]\n",
        "    freq1, freq2 = frequencies[waveform]\n",
        "    noise = [np.random.random()*0.2 for i in range(datalen)]\n",
        "    x1 = np.sin(np.arange(0,datalen) * freq1)  + noise\n",
        "    x2 = np.sin(np.arange(0,datalen) * freq2)  + noise\n",
        "    x = x1 + x2\n",
        "    return x.astype(np.float32)\n",
        "\n",
        "from matplotlib import transforms as plttrans\n",
        "\n",
        "plt.rcParams['figure.figsize']=(16.8,6.0)\n",
        "plt.rcParams['axes.grid']=True\n",
        "plt.rcParams['axes.linewidth']=0\n",
        "plt.rcParams['grid.color']='#DDDDDD'\n",
        "plt.rcParams['axes.facecolor']='white'\n",
        "plt.rcParams['xtick.major.size']=0\n",
        "plt.rcParams['ytick.major.size']=0\n",
        "\n",
        "def picture_this_1(data, datalen):\n",
        "    plt.subplot(211)\n",
        "    plt.plot(data[datalen-512:datalen+512])\n",
        "    plt.axvspan(0, 512, color='black', alpha=0.06)\n",
        "    plt.axvspan(512, 1024, color='grey', alpha=0.04)\n",
        "    plt.subplot(212)\n",
        "    plt.plot(data[3*datalen-512:3*datalen+512])\n",
        "    plt.axvspan(0, 512, color='grey', alpha=0.04)\n",
        "    plt.axvspan(512, 1024, color='black', alpha=0.06)\n",
        "    plt.show()\n",
        "    \n",
        "def picture_this_2(data, batchsize, seqlen):\n",
        "    samples = np.reshape(data, [-1, batchsize, seqlen])\n",
        "    rndsample = samples[np.random.choice(samples.shape[0], 8, replace=False)]\n",
        "    print(\"Tensor shape of a batch of training sequences: \" + str(rndsample[0].shape))\n",
        "    print(\"Random excerpt:\")\n",
        "    subplot = 241\n",
        "    for i in range(8):\n",
        "        plt.subplot(subplot)\n",
        "        plt.plot(rndsample[i, 0]) # first sequence in random batch\n",
        "        subplot += 1\n",
        "    plt.show()\n",
        "    \n",
        "def picture_this_3(predictions, evaldata, evallabels, seqlen):\n",
        "    subplot = 241\n",
        "    for i in range(8):\n",
        "        plt.subplot(subplot)\n",
        "        #k = int(np.random.rand() * evaldata.shape[0])\n",
        "        l0, = plt.plot(evaldata[i, 1:], label=\"data\")\n",
        "        plt.plot([seqlen-2, seqlen-1], evallabels[i, -2:], \":\")\n",
        "        l1, = plt.plot([seqlen-1], [predictions[i]], \"o\", label='Predicted')\n",
        "        l2, = plt.plot([seqlen-1], [evallabels[i][-1]], \"o\", label='Ground Truth')\n",
        "        if i==0:\n",
        "            plt.legend(handles=[l0, l1, l2])\n",
        "        subplot += 1\n",
        "    plt.show()\n",
        "    \n",
        "def histogram_helper(data, title, last_label=None):\n",
        "  labels = ['RND', 'LAST', 'LAST2', 'LINEAR', 'DNN', 'CNN', 'RNN', 'RNN_N']\n",
        "  colors = ['#4285f4', '#34a853', '#fbbc05', '#ea4334',\n",
        "            '#4285f4', '#34a853', '#fbbc05', '#ea4334',\n",
        "            '#4285f4', '#34a853', '#fbbc05', '#ea4334']\n",
        "  fig = plt.figure(figsize=(7,4))\n",
        "  plt.xticks(rotation='40')\n",
        "  ymax = data[1]*1.3\n",
        "  plt.ylim(0, ymax)\n",
        "  plt.title(title, pad=\"20\")\n",
        "  # remove data points where data is None\n",
        "  filtered = filter(lambda tup: tup[1] is not None, zip(labels, data, colors))\n",
        "  # split back into lists\n",
        "  labels, data, colors = map(list, zip(*filtered))\n",
        "  # replace last label is appropriate\n",
        "  if last_label is not None:\n",
        "    labels[-1] = last_label\n",
        "  # histogram plot\n",
        "  plt.bar(labels, data, color=colors)\n",
        "  # add values on histogram bars\n",
        "  for i, (_, v, color) in enumerate(zip(labels, data, colors)):\n",
        "      plt.gca().text(i-0.3, min(v, ymax)+0.02, \"{0:.4f}\".format(v), color=color, fontweight=\"bold\")\n",
        "  plt.show()\n",
        "\n",
        "def picture_this_hist_yours(data):\n",
        "  histogram_helper(data, 'RMSE: your model vs. other approaches',\n",
        "                   last_label='Yours')\n",
        "\n",
        "def picture_this_hist_all(data):\n",
        "  histogram_helper(data, 'RMSE: final comparison')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJ-1YaKQYysO",
        "colab_type": "text"
      },
      "source": [
        "## Generate fake dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAhYNwMUYysP",
        "colab_type": "code",
        "outputId": "554c10e9-f3e9-415d-ea80-f47b6f97f6a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "DATA_SEQ_LEN = 1024*128\n",
        "data = np.concatenate([create_time_series(waveform, DATA_SEQ_LEN) for waveform in Waveforms]) # 4 different wave forms\n",
        "picture_this_1(data, DATA_SEQ_LEN)\n",
        "DATA_LEN = DATA_SEQ_LEN * 4 # since we concatenated 4 sequences"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KW5tlswdZ5Dls+WzXKjqMkhSFEPUs8oaqQ+Q5hGzXbjTMKsw3NJe2ajg6FXX9\nfcrKXI3XhTgo9rzCTFff91Q8jEJdDbQXklSYJxzmxxLiYWEsnfsG4csvbYMD8NaXz3f83JJk70KF\nmSQrYi4B80wygmSE3zVTuP0GMdha6FK13LOYQpjn8PETOagB9zOWZHdJ9kwyAiHE4Y++eMla4N7I\n/Mm/Xcbl7TpU3ehJpGaiIpqaEfhoDVJhrjvI4CSfclQSuEVb77t9PmlV7MaFjVIDfIizJLs7PhKB\n7RFrnbOuaReSTaW3wuxHkq3rBqoNzapczrTGV5Kq6Djx/IrZd+nXeO5MrozjswkIreqW5HNs0Xq5\ngaQkWOOkLNMvSlMos89dQWbMTb8ev5QHADx2Me973VZrah1tWNEw73sOsz3plImKvsZKFWUFiYgA\nkQ9ZM9XHbZ2+UW5gNhXBZDyMTZ8VZlJtt0/GCPN+AuZOLw3AvM+N41qbBcwUyIqG9XITRyac+5cB\nuxX7eF2Ig7LVGoT++999zDL8IhDpR5BBWb6qICqGXIMxwOzVq43hbLhBuLBVw4GM1JFVBNpZxt3o\nYSYyb3u1tJuJeHhX5OD7kRUrYO7shY0IIfz8aw/jqxfy+OyLG4F+Zz/TLyHE4Re/7TBkVcdTV/wb\ni4wbxA/BKUlHngd+pIM0kMVqtdmSwQ1ZYea4tinVbXNJXMnXfFXXRp31cgPTiTASER4iz1E7vwK2\nCrPtfhUWeOqxULKTJFsIQdUNqrE2JMFIrsf0mPZf/ukXz+Pfz5vjHi/7TMStFWUczLbXZURxQZs0\n2ijJmE7YezdDEHmOup2irmhQNAOpqICUJCDE0bsL7yeeupzHRFxEpaHihRV/KhS7JBvwH0w1FL3j\nGkpFBRR9SKpLctsUrt3DPF7HaKPcwHQigplkBJs+k05la5Sk/T4Xou9hVjuPD+A/KbJfYAEzBeVW\nttFujtRN2nLfG6+H2aBsVRW86bYpfNddMz2/m2rNSQ7SVXy7qnS4PTsRE00jg6CrcvuRC5u1DjM2\nApGX7kbQ+tJ6FWGew+EJd6XGVFwMvL99v3J+o4aUJDjORH7bqxbNmeYBVuN1wzDH2LhUmAHgra9Y\nADB+i/ZBaCg6phNhPPBjL+v5HQmy/Li50kAqzFVLkt0ZjAH0LtmyYkodSULzjrkkDAM4tz4+HgI7\ntSaysTA4jsNELOx7fivQKVWM+FhIkuMwqDFb98ijcCuYG7eROKQv9s13zWGtKPvsb9U692+YXAOU\nkuxKE7OpTgVPPCyg2qS7bsn1nYmKCIU4ZGLiriSb9xJNN7BZaeKNt5lrOb/GgHbTL8BUadAGU6pm\nrtfsAVkmKqJYV6nbXcp1xXp9K1RvAAAgAElEQVSGxsMCOG781umb5QZmkhFMJSK+19VkX9gT5X56\nmJ0CZklkAfMNC3lw9VtIkmA66AXSfsQwDHMQuksAO9X6eZCBUb7WxGTcvXIJtHtnb3Tjr6aq42q+\njpsdAmZg96q8Z9cruHU63nfczmSCVZgBs4fuSy9t4fXHJh1/TxIbQfaa15oadAOWbM2JiBBCRAjt\n6oKjoerQd2EOeNCUZBWHJ6I9C24AVk9z0BVmsldqjpJsfy7Z5riW9kL21tbIvQub/pyKR5laU7OM\nnDIxETs+njmFVuCT6lpIyopOtVgngXVnnzl9UqM7YOY4DomIgHJjvO6P+WoT7/imQ/gPd5gB2ZU8\nfZXZlOvaje+IyoK+wkwMvwjxCE+tsiC9tKlWgmwiFrZMpsYFkhy9ZSaBqUTYd8BcUzRL8g4AsbBA\nLXm3riHbfSodE6HpBnXiqNxQLbOvUIhDMjJ+E21Ksmk8N50MY2tQSXa0s8Lsp4e5O2COiTzqzfFT\nc7KAmQIyk5aYbzhBHqrFMbsQB6FYV6HqBqZcAtjpliSbjGQJgu2qYjk8uxEbwKFxHLmSN/sub5mK\nO/7+tpk4nlwqBGqMYRgGzq5Xcduc83cSpuLiWI5N8cuXz+VRV3R83zfMur4mGw8HGjCThVGyz32O\n/H63jG0Mw8B3vfcZ/NPX13bl84PELvXrhlSYC7Xd2U8ke985ssifS3Zd6ZRKZluV1HGSK9abmmXk\nlIn5c9ct1MzKlMDbAzLz302NPmC2Lyb9yOatGcG26zEREVCRx+f5JSsaak0NEy3TOQC4tkM/Ms9c\nrHfKfQG6sUWGYWC9VZmzEwvz1MFYsavPPRsP++qT3w+QgCodFXDnfAqnB6gw23uYZ5MRy4zPC6e2\nhrSl3qHbz6W62iE3TkkCygEnMveSpqqjoepISgKmExFsVZr46rlN6veTdV5PDzNthVnRmCSb0cbp\nwdVNu8I8PhfioGxWyWxD5wozCWyDdMqmkWSTm/aN7pS91hq14jYn9h33LaLa1PCxE/5HSLixUW6i\nJKs4PpPo+7rJeBiVhnbDu82f3zTl63e1nMOdmIyLgVbjnaRZTiQlwUoiBk21qWGj3LTm3o8y/QJm\n8jzY2aXnQa3pPLIIoJejyorWEXCTKpCfGbSjTtW2WM9ERd+mX90eD2QfNyl6ZK2xXXaXbB+yeVKF\ni9uUbYmI4Pv4nLxWxIs++06vF/aRPwdaAfOyz4C5oy3BShp5XwOyokPRjB5PjXjEjySbVOfMYzQR\nE8euwtwOqETcuZDChc0qddHBMAzUmhpitmtgPiNhjXLSQsNKDHZKsgH6tXZZVjru00lJHCsnc7sS\n5a6FFADg1z72IvX7y7IKked6vBb8jM+zj/0C/M+b3y+wgJkCkm3sJ8mOh3nwu9gbsVFu4OS10Xzo\ndUMCYbeAWeRDmIyLWPdpTuCGqhso1BRM9nHIBpgkm9A2k3FOAB2fTeD4bBxf8zGz1AtSCZ1O9k9q\nTO7iHOj9xNJ2HYcmouD7yNeDXpxZAXOf+xxgZqJ36z5HWlrO5Cr4uQdPjXS1xhzB5byvsjERkhDC\nyi45vhPTL8kpGKM0PKorWsdClA9xiIV5arkk4Z0fPoHf+ufTvt5zvbD3T2ZiYV+GTGZCpPOZQgzS\naPqYZVU3TdX49jVMjhfN+0lQYlcBJCT/AfPv/ssZ/NZnRvP4EOPPiXgY2ZiIWJinrjArmg5NNzoS\nEn4qzOQ13UahcR+SYbLWybZGj03Ex0+S3ZbsCnjZwTQ03bBczb1otjxj7OrM+ZSEfFWhCqhI8s9u\nbpjyWZwqySqStqRIKjr48+vKdi3wUYHDYg+Y33D7DH7y/iMoygp0Sq+ekqwgKQkd5rxh3z3MXS7Z\nYX4s19ksYKagfUK6V5g5jkNKEnath/n9T1zDz390NB963awUzEB43qG3jzCfimA1oHmSW5UmDMCx\nl9AOeTCO43w4P5DFQD9H8ZctpvDCaiUwg7SS3Cv7cYL0t9/osuylfB1H+pijAWbAHGRA+exyCRyA\nmyb7f28yIuya6Rc5T06ulPHopR38xVev7Mr3DIui6ZAV3fV8DnEcjkxGcWlrd8ZvkXvYoHJfwKzC\n2YMxYLAK5nPLhZGd31yzBczZliSbdsErK51SUqC9v2kCXtLbZ1+ISj4COnKMOyXZvO9rb6VQx0vr\nFSpn7usNMciaiJvGbAcyUeqA2VnyTpJGNBXm3rYGwF8P8/mNChIRAXOttcdE3DSW08bIWLRkc1G+\n92AGAPDslQLVe0klN2G7Ty60lG00VWYyIslefPFTYdZ1oyexmYwMJsl++KVNvPHPH8UXTgc7mWJY\nyl3KsOlkBIZBrxRyTAz6Givl4JItmuaI43QdACxgpqLSJD3MHpWXqLhrPcxE0rofsjZXd+oI85z1\nEHFiIS1Zc2aHZb0lMZ71qF4SWdBu7cMzuQpeWB39OaZEbpboFzAfSKLW1AIzAKINmCctQ7jRrSzu\nNoqmY6Ug44hH4DoZD6Pc0KgfbP0wDAMPndrEKw6lenr6uklKwq5J2roTjg+f2x65jD5Adz4fnYrh\n0i5Jy2v9Ksw+eph7goUw7ytgbigatirNQCceBEm1qXYEzJpuUAec9S4pKWB3IqcImBWtw/ALsJt+\nDVZhTkZE38cnX1XQVHVc3qXkzTBsV0jAbC7YD2R9BMwOct2ID9OvetcccoIpyaa7hs625kCTpMhE\nPAzDCN7sby8p1dvjIDMxEbdMx/H1q3QBs5Nh7nxrTCLN+o/I84lcHzAl1fbP7ke1qcIwOtuMklFx\noArzv7bc3P2Y0l0Pus0B/Y407JasA/4rzJJDDzNA/yzaL7CAmYKKrIJD/x5mwJyTGKRRkh1SSRrV\nhYmdq/k6DmT7y0kX0hHkSo1A3HDXy+Y+mfVY6MdaIyd2I2B+9GIeb/3ACbz9gydxbWd3ZJhBUWlo\n4ICe6omdu+bN3tnTuWBGzNAGzNlY8O7Cqm7gTEB/x/VgpSBD1Q0cznpUmONkZvbw+2qj0sRSvu7q\nym0nOUCVi5bu456vKSM5RofmfL55KoZcqRHYXGN74oC4ZNsz+2EhBI7zM4dZ7wkWEhK9HBVoL3q3\nKo2RS2yomgFFM6zebNKPTNtX3i1ZB9ouzDSzmB3HrZD301SYW4lNuxIoIflLaORKbRXX6dzoJXOt\nCnNL0nwgG8VKoe7LhdyxwkzVw9ybkADMRDLNNWsYBl5ar+D4XNuXg8xk9yvL/o1Pvog3/vdHfZk1\nXS+6vS3uWkjh3Abd87Qi9xabSMBMU2FeztcR4swCC4EoPWmeC2WH+/QgLUWKpuPktWLHZ44KvQGz\nT1M0WbWSEISID5fspqr1SLJjPpQ0+4lAAmaO4/6O47gNjuPoO833EZWGOZoixLkHgICZ2dmt3j6y\nKF4rNvCuT53FJ08GZ8gUNFfydRyecDaUIsynI2hqhtXDNAy5MqkwU0qyd8Hu/svn8ta/3/fEcuCf\nHyRVivN5plWtD+L4APQBM/l9kImnr5zfxls/cALPXKXru9prSAJowcWUjdBenA1/jMgiYDrR/xoC\nzB6wckPblQDJ6f45iv3sTs6i3RBpe1AGZnZn5ppDdY3jOEhCiCqYA8gM2+EqzNcK5t+maMbIVdXq\nrQUfue9PtNQrecqkc91Bsm6ZSlFJfvWOGcH291NJsp16mH1K5u1ByZm10ZPN56tNiDxnBWMHslFU\nmxpVq4lsJY3a+0fkQxBCHGWFuXUNdfcwtyrMXve3taKMsqzi2GzbmNE6x3wEzIqm45PPreJKvob3\nPHRu5KTzpboCPsRZHjCpKL3KoeIgyZ5LtQJmCn+Hazt1zKcliDanesuckGKt3Q72O12yKw3Vl1z4\nFx44iaVts7JMa1h2vSD7ISG1zQ0B+qKD6SLuUGH2IcnuNv3y2x60Xwiqwvz3AL4joM8aOcoN1VOO\nDZgXYnGXRnKQB8jjl3fw+TNb+J3PXhjJqpmmG1guyJ7VsfkUkeUM38e8XmogKoZcTawIMcslO/ik\nRr7axK3TMbzmaBYnV8p48Nm1kZWjVJqq9fBzQxJ5xMJ8YD2yZVmFEOJ6KlrdxHbBPG+tdY7949Mr\ngX3mbkL+9rRnNb5VMQvgGLUdefufF4Apr1N1w5I0BomTB8Qo9rPTOIrfPGXOOQ+qj9nuTNt2ye6W\n/NKbrchdY6UAMyDzUxFftS16NwMycQyKblOnqbiZDKI9n2RF6wmmSDWTZjHZUB3GrfiYlV1XNIg8\n1zHWKhERoGgG9eiw1aKZ0MhERZwZxQpzVcFELGxJmv2MlnKqMAPmNeAnYO6+BuJhHppueCZFSOB0\n0LbWISoGP9NSVosydAP4tmNTuLRVxadPjtZIPTINgBwjco+gSZh2Vz8BMxiLR3iqtfK1Qr1Djg3A\nCt5pgnbLsKzLJdu+bV4YhoHHL27j+1+xiFcezoxewNwle7cqzJTrgrKsWEZqBF9jpZzmMI+pwW4g\nAbNhGP8OIO/5wn1KpaH1NfwipKO7I8lWNN1aoD12qe1cvDyC0t+NcgOKZnQ8RJxYSJuLl7XS8H/D\nRrmJ2WSkw1zFiajIgwOdlMcvW62xVsdm4ri0VcN7vnARD50ePXkVYN7E4hQJoGxMCLTCnIzwnseI\nmOcFGTCTgPIr5/K41lrgj7JUqNvEww2rGh/AviI+DV6JFPt27YY0zb6Imhphx3RyHvWrMB/IRiGE\nuMD6mO3nbK2pQ+S5nrYXczFLGzA7SLJ9VjBXCu2/bdTahWSrR9U8pycT/gwFu+dUA22XbCqHX1Xv\n6RGXfMzKtjt8E8i1R3uMSLLwW2+dwpm18sjJ5gu1ZsforgM+AmbiBt8jmxdDVMk82fIB6O1hBuCZ\nOCJJD/s5QsyT/NyTr7Yqlz/5LUdw10IKf/HwxZGqMhfrSocpVDzCQzfonqFOATMAJCidyK/t9AbM\n5PNoroGybSQWgdyzaZMaxbqKuqLjluk45lL0I7GuF+Q5TPax36SNU0EwIoSgaAaV07as9AbMmZiI\no62E8TjBepgpqFBWmNOSiHJDC8xZmGCvIF3YbFcrRk0CB8AyPSP9lW4Q18MgZjHnSg3MpvobfgFm\nZjId3R0n8+1qE1MJEcdn49bPHr9cwMaIVV2AVgKIIjDKRsVA+mMB0ifjfQ0B5qIwyIA5X1PMynWI\nwwPPruL5lRLu+5Mn8MTl4MZmBQmtfJ2YewSxr/xWmAGgFFBvbr6m4D2fvwhZ0TquzVumzWtp1CrM\n+ZqCv/z3K7hzPoGDWXfZvBAK1inbHgTUlN6+McBcNJUbfnp0neWotKzsyFbQPnoVZnN/xbsk2TTP\nHMMwICt6TzDlyyVb6a0wk2NGE9DVXBQAAH3Sd60oYyIu4t5DaezUlI6e5lGgUFc65iAvZuhnMTdV\nd5UFVULCrcIcIUq0/p8hN3sD9tQAyUQi9T0yGccPvHIRKwV5pNYNZVntqEC2Ewp+Amb/TuSGYWCj\n3MBssvceG6cNmC1JePv7s637AK0yiwTI82kJ82kJuZJMPbLpelBpmEoUkswjyYECxTpX0837XHdi\njnwWTZXZSUnzTUcn8flfuh/HZhMu79qf0K1gh2RtbQ2aNroVHTu6rqNQ6HQALNaayET5np93I8K8\nAK+tbyMTDW7XXt3qfHjcOiXh/JaMTz63iqWNIn7qvrnAvmtYVrdMmTin1PvuL8MwwHPAynbZc796\nka82cCwWpfqcVCSEjWJ16O+0YxgGtipNJHgD89H2ef6FM1v40tktPPwzdwXyPZqmBbLdxVoD8bD3\n+ZwMc9isyIF8Z74iIy5yVJ8VFznkA/peANgo1jCXEHBkQsInT+RwecOUJj52bgMZXoGs6rjJo+f+\nerJZrILngEa1hGbNvSKvtx5m64UyCoX+ig7P7yyY/Y26XEWh0H8hEdLMBcTaVgHTojL0efk3T+Tw\n4IktzMeBrVL7XreQDCHEAav54e8RQfLschmVhoafeNUUyqX+ffEHUgLOb1QC2f6NfHvfyKqBCRHI\n5Tq9LCIhHflyvefn3eiGKTlVG7XO16pmX+ba2pqnGgQAljaLuHlSwrnNOi6tbiE3Nzo5+EpLrSBX\nS8jlzGslEQ5heWPHc/+QGb+q3LkvKy1lwcZWHrlc/0VzpW62Ctnfr7b60Ld2ip7bsFOqIsJ3HmOl\nZt67llZziCjeFZwrG0VMxQTMhM198djpK7j/prTn+64X+bKMA5lwx9+YivA4v7qNXK7/PW1tw7xn\nVUs7yOXaSZAwZyBfrnru3/VtUxRZLmwjp7bl6krNXMNcXV1HuOm+Dbkt85quFHaQ481rUzcMcABW\nNneQy7n7Qaiqam3fmeUtSAIHrZKHIZtTKa6srCMkj8YzabNUQzzcPo+1url/llZy0DL9PS/WrH20\nDaViMygMGciXa32PEUlKKXLv6yIhHdul/u8HgPWtndb355FTzGPMyWaC4sLyOmYF78TM6SvmeRZW\na4hzTSiagecvLGOOokhzPdjYKSEmhrC+vm79LCqGsLpV8Nw/RDKttvYxOS/l1jWwvJpDsk8SXTdM\nY0WnY0SgeY6MAgcOHPB8zXUJmOfn56/H1wTC0tISMplMx8/qqoGb4lLPz7uZyzYBrAHhODKZ4Raw\ndpQd8yEbEczZZi8/lEWuvIHTG3Wc35bx699+fGROSmPdzGrNT2WQyfTPLk3Ew6hqvOd+9aKpA6mY\n9/EBgImEhIrKDf2ddsqyiqZmYGEygbsPz+It95QQ4UP4p+dy0A0gnkx1mFYMSqFQCGS7ZY3DQizi\n+Vkz6Q1czDcC+c6aCmTj3t8JmK8rympgx6iiXMF0SsKPffNhfPnC83h0yXxwNgweb/3QOQDAyf/r\n/kC+KwiaxhZSkoBsNuv52qgYQhPC0PtK582F2vz0hGdle75uPkB1wbzmhj0vp9NlAFt4ZkXG41fK\nCHGAbgAL2SSysTIqaijQ63VYqlfN6s+xxSlkPIzZbpsv4t8vlSDFkz3VXL9wXTF3LCJgbq4zWTqR\nXMNqUe75eTekH3oqm+547exEDaq+iYmpmR5DMCc2a+dw301ZrFcUrNU4TE3PdPTc7iXNJXOhuzg7\nhbk58/yZTl5A3ejdb92QUTrTE537R5PqAM5BSqQ8P0PDZaTiUs/rhNCLEKWY5/v10CqSUaPjdQdr\nYQBXICUymJub6Pt+AMjLl3BoMoFvufMIsp+7ig+f3MF/fPWtI3OMqspLmM0mO/7GQ5NLyDc4z/0T\ny5trnvnZaczNpayfZxJXoXIhz/eHL5vX8aGF+Q5Z+GJFBHAFUjKDuTn3e7C0al5DBxZmMJdtJy+S\n0hnoQu9xt5PL5azfL5eXcWQyjvn5eSyWBQBXIaUy1jm712xUz+J1B6at7V3cCQFYRjSZ7djvTnBi\nGSLP4fCBhY6fZ+LLUDSj7z4qtxJeU9neay2bvAZZ0T2PsXjRTHAdWphDqqVk0KU6gAvQRO9rEADk\nq+Z23HV0ATcf1PCXj63hY2cq+L233OH53uuBFtpEKhru+FuysXNQQmHPv4+ogmanspibm7POy6lW\nLJOdnMJUHyNQ0poymUk7fpemaZCk0Uj8BMFo3DVHGFMW0sS0x4xfwC6RDFYqTaQj7/meY/hvbzmO\nX/q2w5YTnulOOjo29+WWTCdJIWGfjIuByC2dpHNuZKMCCgHJjAnkb5iKh8GHOLz7zcfwG2+6Ge/4\nxkUAwRpYBUG1oVJJb7MxETs1JZC+t7Lc68ToRjoqBtofm68qyEZF3LOYwhuPT+JVh9NISjyeujKa\nrtnlhkYtXw+q37vqp4c5QmSHwaiGRN5c+D7a8me4Z9FchGVjYuseMVqtJ2Tu+0zC+5lwKBuFbgRj\nbnhuo3MmulNAm5TopIpuclRL8kshy1Y0HeslGQuZKN5w2ww+dXINt//Ol7C8MxpzStuS7Pa1NJkI\nUz1zug3DCJYkm0Ly62SGA5hO2TQ90LWm1jP6z69/wGpRxnxaQjTM413ffgwvrJTw/MrouGV3S7IB\nczoAzYxe0qPePes6EeGpHJRltznMxByUsoe5+/tTkoAyZbvcCytFfO3yDt5056z53T7kzteDakPF\nVqWJQ7Y+YmsbKcxT3doZ4xTmgkRyH3a4hhIRgeoYW07ztuto0kdrBmC6eQshDlOJCI5MxvF99y7g\nkydWR0aW7bSP01GRyvSL7J/uefPh1jntZW7Y6HOMxpGgxkp9BMATAI5zHHeN47gfD+JzR4F8zZRs\nElfnfpDegaB7ZJe2zVl033xTFt9xxzTiEcFyyAVGy2yF1rAIMG9cQZhKNVQfAXNMDDzBsNX6G8iN\nGDD7pUk/88gFzE0NibD38ZmIiVA0IxCTND8Bc0oKts98p65Y18ufft/teN8P3437j05gySZxHSUz\nHD/93qmAZr9XGxqiYqjv7HSCtWgPqIfZvjj80Vcv4q9/8E7cdySDVx5KYTJOF+BcTzYqTWRjItUi\ngYxnC+IefXqt0tHq4xSM0S4kSVDdHRDGrRmn3p+RK5nuvosZCW++u11deGlEpjdYY6UinYvlbYpj\nYY0c6h4r5aeH2aG3DzADLJoe5rqi9Sxk2z3MFP2bsopKQ8V8SwVxvNVPuFMbjetJVjQ0VL1nGkAi\nInQ4wrthuWR3PfuTEbqxR3VFA8f1LvZJcOV1jGTF2XQsKYnUz/xPnVhDVAzhHd90GEA7uRPU7PZh\nudp6Rh6ebFfQ/dwjyrJLwBwWPJNyZIxe2EENQWv6JSumOaJd4RcReSQiArYonytrRRmzqYj1bDw+\nl0RT1annue825YbaMbYLQMurx0fA7NLD7HWfc3OqH1eCcsn+IcMw5g3DEA3DOGAYxvuD+NxRgLhM\nElfnfpAKczHgAOnZ5RKOz8Y7Tmr7gnqzPBoPQMC8QXLoNXlwYjIuYnvIai/pxYs6GOA4kQmwakog\ni+HJRGemPE0cM0dIAWAYhjWH2YuJgMYW1ZoairLqaQRHSEkCyg0VegDHqKnqqDS0nu+e7VKMjNL4\ng7KsUik0gOBmv1ebdMaGQPAu2XZzne+5ewaxMI+/+aG7cGQyhrlUBCsBVGeDZL3UsAJhL8hc640A\n7tGnchXcs9ie+eok8SYVZq/7G5kTOxHv/DsSlA7BQHuk1GImitfcPImfuP8IgNGZU1pXel2MqSvM\nTefqI6m80ATMdaW3Qkw+k6ZC7VRhTvhJaBCzolaynyT0d8PdHgA+fWIVJ6/Rq3bIgj4d67w3R0We\nyoGZzBuXuhbrCYmnSvKSsWrd7Wy0TuZWhbvrOjTvyXTPzPWSjMVM1LqnxizDseCP0Z//2wU8dnHb\n13uu5k21yKGJdsDsx3jOvcLsbfpFjq9rhZniGqg5GBsCpunsdoXuubJWMlUahJmkeU9fD2DCSxBs\nVxodxRrATNrQXOe1pnPiNNxSfXlVmOsuSaNx5cb4K4dgreQjYLbs6oO72TVVHS+slvGKg51GHYrN\nvW6D8sK/HpgW9TxCFD3VEy1J9jDBK3lodWeZ3chEzRmyQY2W+sKZLXzsuTUkIjwOdvUzJgMc+xMU\ndUWHAbqERlBzfk+tlaEbwF3zSe8XA0hK5tiKIGRpZNuzXYuyQxOmxOz1xybN141QUqPU5Uraj6Qk\nBJKgqzQ0Kjk2YLo/x8J8YOc1uRZ/9Q034ZbpTiOjW6Zi2KkpI1VlNsfY0QXMJLAe9h5da2q4kq/j\n7gVbwOyykFRbzqf9IO733QGzH0koGf2zmImCD3H49f9wK8JCiEpO64emqvuaa0uQu1yyASAbC6NQ\nVzzllLKLZD3Mc+A4uoC51tQQc1DyRESebg6zY8DcClYorj0yspEs9nfzebS8U8OvffxF/O9/8yRO\nLNMZ3JFjmumSZEtiiLKCT+SgvfuItsLstNAnrttex6ihaOBDXI8/SVISqZPkm5UmppPttWXCkoMH\nm8DVdQN/9ZVLeMffP+vrfVd3SMDsIMmm2McVh+on+QwvF/Jmn+olOcZea0fZQaUBmAEztSS72Bkw\nzyZJEnQ01t3d5xBgXus0CrCaW+tJa595uWTnK86J13GFBcwerBY7Hzr9SEUFRIRQoKMbzuQqaKg6\n7j3Yaa5gXxAFUb0IirJM3385FQ9D0YyheiEbVh8RbQ8zsdwfXk5TllX8+qfO4pmrJbz5zpmeTKY1\n72+EAmYSeNAco0FmSjpxcsU02bJXx/p/b3ALu7xLwPyWe2bx0R+/F2+5ZwYAcG1HhjYiPUnlho8K\nsw/5Xz9oVQeEZIQPTpLdVHF0KoYfffViT7Xn1hmzrcE+Tm+vMUedeCdQAXMhkojwQ6uAyHk8m4xA\naEkDnZKEZHHqFTC0K8y91T2AbsYqec7Npcx9EQpxmE9JWCsEGzD/3mfP4pXveRh/9ZVLvt5XaWoQ\nQlxHhSopCTAM75FBbpJsjuMQEbx7kK1xLQ6LdXNOMN3Yo+4kVlgIQeQ5qmtvregSMO+ClPQzJ9sO\nuU9foRvXRwoL3T3MUqvC7B0Muc9RrjU1z/t5XdF7+o8Betm9W8Cd8lFh3iw3rBGbgL9g1A+DrneW\n83VkYyKS9jnMYfoquCnJ7t3H8TCPpqp3FH66aap9JNmtpLpXYtBJpQGAutVH1w2sd1WYZ1Okwhxs\nwPxb/3wanz655us9sqKhLKuYTnRXmAW6CnOjt8cbaO9zrwrzZisRPN3HGGycYAGzB2vFBhIRnqr/\nMsRxOJCRcJVihiAtZ9dNo5fu6lzDdqMZqR5mH4v9ydZibXuIniq3PiI3MlbVdPgHkt2M6EdevdDz\n+yDn5AbFc8um4QtNtTeoFoMT10o4OhntWRi5f29wXgCkwjzRFTALIQ7HZuLWd/30Ay/izx9eGvr7\nhsUwDLPCfJ17mCtN+gozQB7IwVRB+s0FJxXn812GV3uF2bvWu0Dpx0wijI0h79H2eaakl85psZ+I\n0MnlrYA51vl3kICZpkVhq9JAOip0mI+Zhk3BPf8A4NKWeeyfuORPTpqvqZhKhDuSMLQ9wG6maIAZ\nUNFKFWMOwUKUck5wrUH1vIMAACAASURBVNk7h5njOOoKKnHAJS63Ih9CLMzviiT74XObuPdgGumo\ngJUduoSJJcnuDpiFEAyj3cPqRkPVEOJgJZAItMdYbjrLdclawispIqvOAXeKUg5rjqNsdAQbEcH0\nkQi6Rcjet+917na8r9rsCOgBMwnIcUCF4v6/U2siG+u9V9IoWUh1002SDVBcxy7HeCoRofIy2Kqa\nY6TsATO5njYCDJirDRUPPHMNH//6iq/3WQFrVwI31WrN8U4aEbPPzvVGWDCvKa+kEbnHdH//uMIC\nZg9y5YaVQafh0IRkGSUEwUsbFaQloUcC+O4334o33TaFo5PR0QqYZRVJiW7hHYTbrptTpRtE/pUP\nwPiEfMZ733onDmZ7x4iRjP7fPXENX18eDUfmZ5aLSEtCj/TViaBaDFaKDdw05f19hKB6pwGb9DTm\nHKzbTZQ+e2pj6O8bloaqQ9EM6msoJQmQVd3XIsiJWpPOOd3+vUGafrl992Q8jGxMxPkRqTCTyiSt\nigYAppPhoSvMRLaeiAhWgOCUJEz6qDBHhFCPFI/8N42Ls1kd63w2zqfpHI79QNxeV3bq+MPPvYQt\nSnn7dk2x+g0JSUpJs1v1EjAlu14LSTczHfJ+L7mvpre8ORzebxq7eR+f7WoT6ajQU2HfjQTuZrmB\nwxMxLGaiuFagW/8U3AJmynOwoeiQHHqQaa8Bs8fcXZLd8KheNtwqzJIpN+5XPQXMe0ld0TFlW9tx\nHId4mKdyqffDpu2aObdOb8pXdHAxN7dR8KwwG4aBfE1xlOtaXgl9PqOfSzYJ8LwSE7KiOV6Dpvmr\nd2tGt0qDbM9kPIz1ACXZ59YrMAzgmSs7+NBTy1QtCQCw1Xqu9FaYzWPmpVSoOriIA7YKs8c5vFlp\nguPc11fjBguYPSjU1B45Zz8OZ6NYLgQn7zy7XsXx2XjPQ+G22QT++Htvw2JGsvqs9xpVN7BTp68w\nt90ohwiY+yxsnDg6FQXPAS+uDu/kmndwx7YjhDiIPIfNShM/9o8vDP19QfD1qyW8/FCKqse83fM2\nXOBa8aE6ANo33yCSGm49zAR7/1yKsgK+mxAH9+6+PjeCUjGYVV76Y5SMBLfwrjS1vj31RyboF+G7\nTb9AyI2ZRGToCrM1fSAiWBXm7t5NgN4Uiixku58r5J5MV2Fu9izUFtISNsoNz2DBDyTpda0g4/2P\nXcGXzmxSvW+rqvZUPmgl61aF2THg9ZZU113GtQDms0r22L/9zjPaCvN2pdnzbDLH0AUvyd6pKcjG\nw1jMRLFCqbBzrzDTrQvcxnbR9nkT069u+NZzW1a9ZPu6Y/WSPEe8grktFzkrzcglv9j7db/3vV/D\n+Q269U+xpjg+i0zTLu9zuKnqjs9ecp/q93dapl8Okmza1pGayzEmaxuvoN8pYAZM468gTb9Or5nK\nP0Uz8DufOYMvnl6net+GS4WX1q+g7nKfiVCOldqqNDARC4/MXPfd5sb4K4egKKs9Yw/6cWgiCkUz\nAsk+PfDsKk6tVazxRE4spCXLyXuveeeDL+LSVo16MUkcSIcKmFV/AXMiIuDuhSS+tkRnTNIPr+ol\nYN4ARwXDMLBWauCmSbpqLx/ikJT4oSvM1Ub/gKgbItXPB1Bh3qkpEEKca0XQLn2m7YPfTcjCZpLS\nUTwZUJ98mXI2d/t7g5N2VhtqjyTMzkI6Esgc4yCwpLYOizA3ZlNhbJYbUIdIopIAKSnxfSvMfiTZ\nTq71fu7Jm5XeCvNCRoJhBNffZxgGdmrNDtntNcqAbLuq9AbMZP94yXX7SLJjYd5z7FHVcp/tPa8l\nCtOvfpLwBKW6Y7va7Knu0cqF/dBQNNSaGrIxEQeyUawU6lRGniSg7W4FiVou1R5jnVzGdtHL7p0D\nXoBORSC7VJhJAqDg8fwiqpPuayge9naQ9gsJzt90h+nZ8Qxln3mhrjgmkmmSNiTh7VRhppJkkx5m\np9FsIl2Pbb2p9TjdA/RO3/0C5iBNv87kyh0jHVcofSDcki5JyucAccnuMTcUKHuYy72GY+PM3q8Q\nRxw/82MB4FDWvLCu5ofPPpGeyjccn3J9zWImgpKs7tqoCD88uWTKjmkdqNtZwsGrEWRh42cO3KuP\nZHBqrTz0PiMV5gyFAsGPrH+3qCs6VN3wdT6nJXGoYEzTDXPus49gLBbmERFCgczoztcUZKKCa0Xd\n/pAKIkAfFtIX391b6kaaMpPcj41yA2VZs+5dNCQjAUqyPc6PhbSE9dJwAWdQ1FrjhmIOUk43FtIS\nNKPd7zUIZZsku18PM60cdafadDzH/PUw9/Y3zqfN1pSg+pgrDQ2KZuC2ubbnAk3A3FR1FGVtYEl2\nv4A1GuYtd1k3rAqxw3ktid6mYW6VH8D8G2hcss3j01t5CrrCTObRZmIiFjMS6opOdS+ttXwTQqHu\nsU6UFWZFtyphdmhVFm4VZnMbQp6GUg2XHuZ2S1F/VUk72Ol1qvcypfPLZqWJsBDCX771G5CSBJxZ\nK1O9rySrjhVmGlMp8vx2DJhb53WlnyS7Tw8zMTz0VgE4m37RqhCu7dQRC/M9+2AiEQ6kZYxwcbOK\new+m8a5vvxUAcHmbzrNjo9xEiOvdx9box0b/baw1Natv3g51D3Ol4cvPY7/DAuY+GIaBYl2hHvEC\ntO33hzX+qjZU1BUdv/y6I7j3QMr1dQutzNdey7KJvGoxE8EvfNthqvcEUmF2mLXpxbGZOHTD7E8f\nhu1qE5mo0GM64gTFS3Yd8oDzFzAPZypFFo5xH5JsjuMwERMDqzDTzn/eqjQDmf08DMQAj7bCbDmx\nD+F8eyZnPpxvn0tQvyclmX2Uw+4vncwF76NKWchEoBmm8VeQ89MHoV8g5QYZSThMldxu+iXyfVyy\nKSuoeYfqIwAIvOnC7BXQVRsqak2tt8JMnkcBOWWTKpU9YF7e8e5nJ8FId8BMLcluuidiY6J3hbmv\nJHvIHmhaSXa+6iTJph95RMtOlbS9hHGkpV565PyW5/uqTef+UlrTLTcHZFqVRa3pXCEGTLWRlzFb\n3WXGL7muvBK+lpqoO2DehQrzdqWBqVYLxu3zSZzJeQfMDVVHrak5mnVmY6JlHOgGSRg4SbLpKsx9\nAmbKeeh1B+M8wB5Q9t/P5zcquGWmtyVyovX3B/U82iw3MZeS8JP334RXHcni8hadZ8d21TRV6w54\nrZnrHtd6zeUaTEbM93vdZzbLDUyxCjMDMOW+Tc1AWqLvbZxOhCEJIVwZ0viLGHnNeGRv2ouxvR2i\nfrXljPmuNx7FrdPuEnI7VoW5OUyF2Z8kG2gvQmpDzjrM1xRPs4MH//PLcM9CMtDZ3INSsvVB0pKK\nDjfn177Q98NETOxwIR+UnZri6UHw+K/ch195/RGzB3+Pq8ztvnh/PczDqCXO5CrgYPoi0LKQkWBg\neC+AelODAfSVgy+2grC3fuAEvnzOn1MyACxt14ZKytmxJNk+ephJEEkrs3OiLGuQhBBEPgSe85Zk\ne1VO+l0XNJLjrdZieTrZXWE2/9agnkc7VRIwt8/NZYpktFtvH21CgQRT3dVPgFaS7X6e0IyV6tdD\nnZB4z4Wsouko1JXegDlKN27GD4V6OzC6/5Yp3HMghT/43EtQPfrYa03VVbIOeI8MKtadz+G2yqL/\nPq40VCuw6CZMYcwmK7rjNWgFzB4VZiLZ7g5IzR7m4CrMD72YwydPrFlqkNvnkngpV/b02SHjx9IO\n+3gyHvH8+9qj63rXsNYajKbC7NAfS9qnPMdKeVWYPa6jl9bLOD7bO1FkIh62EgpBsFlpWPeqo1Nx\nXN6iqzCXZcWxAEKbEKi7BcySAI7zbiugWQOPEyxg7gMJMNI+KswhjsPB7PCjpdbLzguSbtqLsb2t\nMBNn8EMObtFukBsZjSOrG0SS40eSbd2sh1xE5ymql7fNJnD/zVkq18x+BBHIlQasMA9TvSRun36C\ndMCcDxuIk3lVsWZvuxGPCFjMmNfRXs803642kYjwrr113QQxs/rMegVHJqO+gsA3Hp9EVAzho8/5\nmxvZDTk/En3OD5IUBIDnV+mkhIRaU8Nb/tfX8dv/en6wDXT4PMBfhXk+FUyFmSyCBFJhdpCDinwI\nkhjquxBUNB1Vl8oRYAYsXvfGrbJz71w0zCMbEwMLmInKxF5h3qkpnhU40h8649AfCngnFKpNzbWv\nnk6S3ephdpRk85AVvW91qtanQk0qzP3ebwUrDvNZS7ISqFKjXWEWwYc4vOUbFpCvKp6JVrfqlhUw\ne8htCw4OzgBdMETG97l5W0hiyDKdcqPhUmEmQfyOVwW2riApCRC7AsIwH8K5jYrvEUNufOZ5c0b2\n991rjr68fT6JuqJjabt/FZO4mDtJsifi3hXWfJ/2InLc+7XjNfpVmK0+d/djZBjus9AtpUmfc3Sr\n0kC+quCYQyKZjMoKYo3SVuuYn3l0KoadmuIp6QfIVBr3gNlrXVBzcRHnQ5zZjtdn7adq5nQOP+rB\n/Q4LmPtApEt+AgzAlGUP28PcrjD3lztkogKiYshX9ULTjcD7Na/u1MEBVuBBQ5jnEOKG7WEmFWYf\nPbKtm+2w2cHtqkLVa5oZcq7w45d28G3/40k8e224at5AAXNUHCoYI1l+PzN+AfJAHu4cvbxdw3JB\nppJkEyXHICPaDMPAl89tB9Jju1X1l7Eli8NhFAzr5QYO+Lhuyfe+9pYJPH1luHFpuVYQ2e9vtvf/\n+x1Bd6E1juqJy8Ob/AHtHmancTRuhIUQphNhrAwRRFZsxnntHmbnbUh6SHbbiWCXCjOF5JgkSOfS\nvefNQkYaWpKt6Qb+4WtXsZw3j998WsLvv+UO/PIbbgEArHp8/nbVWe4aCnGIR3jPyku1obomcWJh\n3vPZ0V+S7W2oY5nxuFTHFM3o+37y9085SLJ1ox0MBUFbemt+F/FVKHv0SteazuPkrFYtj33sNPII\nsM0J7nOM64oGTTf6BMy8p+lY3cX0SxJ5xMK85xqrUGs6BqPknP2jL5zr+35alvM1vO74FN72jYcA\nALfPmS1+Z1rOzG64uZgD5mQQRTP67uOdWhMizzmqy9oqAm9JtlMxhEaS3dQMaLrhuDakUZq81Bq/\ndWymN2Ama4ph1yiGYWClNQGCtI/cNGUqNGlk2eWGagX/dqwKM0XSyukeA5jHvV+hZhC11X6HBcx9\nKA4QYABmlfXakKOlNqz5av0XzxzH4ehkDBcpex4A4F9f3MB3/r9PB9ons1psYDYV8VXp5TgOUZEP\nxiV7kArzEAGzYZhO6DMeCgCgrVDws0h5ab2CTz9vjhb49wt5AMCFreEWoYMEzKnW3M5Be1VJBrdf\nBdGJiVgY+dpwlZBf/cRZAHCcke30fcBgxl9fvZDH//nxM/j7r13z/V47nz+ziS+c2XIdU+YEcTIf\nJqlRa+oDPfTSUXFoqfOJFXPRds9ir+yNIPIh/MOP3oMjE1HfsuYLm6a0LRHh8dCpTfzMAy8OvrEY\nzCUbABaHdPou2wI44pkQcdmGRKS/7JbMNXYboxYNe9+TT6+VIIkhR8f9hXR0aNOvz51ax7v/9Sx+\n/6GXAJgSyB945QHcezANwLuyQ4I4p0SMV0IBMIMttzaBKEVCoS3J7r3vRSjMLvuaflH0YW+79MeS\n/ffoBf+tDW6QRTUxv0xSJoirTdV5TjWFJNswDBRcRh5xHIeYRx9w+1nofA1EhJCnJNvN9Ato97j2\no+DSFvELr7sZB7NRKwExDIZhYHmnjkMT7ev05uk4RJ7z7GMm9wnnCrO5bdt9/sZ8VUE21ju6DrC3\n41H0MDtJsi3TL+9raFBJ9morkD3scI9r96kPV2H++HOrePNfPgGg7ZZ+tBUwX6KQZZdl55GdRGlE\nk7Rye5ZlYv0rzIOorfY7LGDuQzvD5m+xfzArQdWNoWznNysNxMM8ldzh1pk4zm/S9TwAwMWtGuqK\nbs0GDUKeVaibbsR+iVL0c/WjMUwP8xC904W6ClnRO+SibvitMBuGgR/4uxP4rX89D91o99U6jUfw\nw0CmX1EBukHvfN5NW3Lr76Y6mzQz2MMoITbKDbzmaBY/9Ip5z9dmY62kxgDfR7bxBZ9y4W7e9Skz\nOOi3CHEiJQ3XZ+4mjfQiSuEk248XV8v44JMrOJSVPJME9yymcNts3FfAfG1HtpJOIY7Db/zzS3ji\ncmGoRGGtzyKsH1mPxYcXZVmlrzBL/QPCotz/uUbTo3t6zeztc5q/OZ+WsFqQqZ4rhZriuK1PLZlJ\nQsMw759EoUKCMq/rdKemICqGHJMKCQqX6WpDc332Ekm2l6Sa45yfS1EKyXGtj7kczUgcMtay2/Ts\n3oMZTCfC+MLpDdf32pEVzdNYaaemIBFpS4st536P873W0BwTCjT7p9rUoOoG0jHnY5QIC30T4mUP\n9WBE8JZku42VAsyAyjNgriuOEzYyMRGvOpINpD82X1NQbWodSeOwEMIt0wlPp2yS4HcyvaUxNuun\n0uBDHMIe88xJDzMxObQTsXqY3d/frwIaJyqEPveBap92IdqA+ZHzW/hin2vN/jtSfFnMSBB5Dpco\n1vQVWXVNfNKMkCvUFGRcEjOZqNi3yMMqzIwOBqnIAbbRFUMYN2xWmlTVSwC4dTqG7apCvdAm1et8\nVcFKQcbL/vAxfOX8cBnnUl31nVgASLZ+8EV3XdVa0m56G+ogepjJ7Ovu+XxOkP2yQ7FgXtqu41V/\n/Lj135uVpvW+ypAP0JKsgoNpGkNLe/EzWJAxqOnXQTKebWewqrqi6Sg3NNyzkHTMcHcTC5vuwx95\nds13FZJcT8P2mZOxTt//cu8A3056yNmqdaW/S7UbkmhWYQZVH/z8R09ju6rgrgX36rKdxYyE5YKM\nf/o6Xd/0L3zsNE6smItCe/JyszL4caop5v2Gxhnfjtl3Ovj1W2loVmVR7DOHmXxXv/PBMvNxqa55\n9TDruoHTa2Xc6XLcFjISqk2N6px81R88jO/4H4/1/Pzhl9ouy3ctpqxrmFTdvJIP+WoTGZf7HM0c\n42pTdb1nxUQehtFfDlpvVW6c7j2S1X85WIWZxgWazMGe7QqYQyEO33LrFJ5eopvD+wsPnMRvfvJU\n39eczZUxb0sckwW8Z/+kS6IuKnobOhX79NcCaFWY3c9hMlqrnyTbq8Itq+5znLPxMIUKQkEm6rzG\ni4f5voZYNOSrTbzt/U8DAA5OdFZJj88mcH6jf4uXlyQbaCsZnJBV94QCQIolfSTVqo6wEHK+higk\n2eQacjpGoRCHeLj/fYCcP05JqwnKHub//P99He/8yAnX39tNCUmFWeBDODQRozL+KjecK8yA93MA\nMJU4buaPmZjYNzHJKsyMDqxeL58BM5FyDTNLb6XQ6HnYuXHrjCnhOL9BJ8smleXnrpXw7ocuAAC+\ncj4/wFa2KfmcV02IhoerMJtOlf4uWEkIIcT1H2ngBTG1maeYr0wq7zQV5nMbVShaOwB57OIOTq2Z\nD7aSz/7NbkqyioTE+0oukOxyccDZndWGe5a2H6SnlmbmqhNkX3s5ZBM4jkM2JiJXauCJywVXgzbd\nMPCp59etHk6gfS5c2KwNNWZJMwx8113T+NFXL/p6X0oSBu5hNgzD7GMa4KFHrrumOtjfTFx0f/o1\nB6leTwwOf//zF60Kmht1RcOlVpvKwYyEpu2a2qwMrvypDyhfT0S8nY37YS6MOivMTqZfgBkQ9q0w\nk+qamyTbQ3K8Xm6g0lAd3WOB9nHyMv7SWy1L6+VGR7VWVjSs2d5710J7rKKVfPSsMDddE7g0c4wr\nsrvpV6x1L+tXAXQbmQS0q2P9nnv9FqM0ctKNsoxMVHSssM8mIyjU6dpdzm9UcK5PYHVuvYynlnbw\nPfe0k3y0o+7IHOZuyHndr3ro5jBNiEeEvjN+SaDkGjB7jJVqqjoMw13lMRETLTM0N8zqnlvAbzpl\nD6P+e/ziNi62qpTdZqwT8bDn8Wknu/tUWPsEjF5rMzMp4b6PG6ruKMcGzIBX5Lm+SSdrpKXLdeil\nxKk1VYg852g6Rsb7eR1jgltgbzf2sid/jk7FreeXG6pmunQ79TAD5tqtX9JK0w0U6oqr9D/tVWHu\nk9QbV1jA3IdiXYXw/7P33vGSnOWV8Knqrup8u2/Ok0cTlFBA2QIkMpiME2LXyMawOMDnsJ/9rXf5\n1jbrn/3tetfrzzlgs0QDzgvIBIEIkpAQEtLkdGfu3Hxvh9upqivtH9Vvdar3rbeqazSt0Zx/QHO7\nu7orvs9zznOOKPg+IcgFGrRgNi0L57Zq2DPG5zh9oFkwH+EMoydsyx984zweW7DNcPqV+5YUekQD\nC/YMc3+mX37k2EBrxqmfQn252cHnkWSPpGRERQHfOlPAb33pNPMh0V0I/OcvnnaYqe0+oyaCNDWy\nfRqWVVQDouD//JrJxiEAWAzIMBNWPkeR7Lmhvbim/d5nl8r48P8+hR/+0+85i3oym1ptGJ4PORbs\nPGL/TSevByMLNkMc7KHnRHt4SDbdYFkWVN3Ee+6Yc7JbvXDvvmFc32Q1n77oblhjWhb+9qkV/P7D\nCwCAP/yRw/jlV+7ueM1mHwxzneIq6gXCMAdZAJvNeU1yfkYZOcxkW0yGWSHsXDBJNik2aeweT7SU\nYVodDNdzy9v43YdOYruuOcUwmeVrL5gTUgRyVPT0gyjUNOQo97rRlOxp7ldt6HRJtuTtgUGLTAL4\nDI/qmgE5KrpK3nlmmNe3bU8RN+SSEgzT8mSfLMvCRqWBDUpz6vhqGW/7k8eRkES8/eZWk488M7w+\nv0rZR1JEQEQUmM9nh2FmRKOxjk/L0JUywyyxZ5idUSPKOTaclJnnqN40zKJ9/1QsAt20Ohp9fkHG\nVw5OZTpmmAH7HKprJjO5o6oaSEhiT8Yv0PIGYEmSFc1gesskJfYxahgm0xMnFo0wZfNFj3MkHYsw\nz9EawylfEARbds/pkr1AYYs3yipEAfjgfXs7IuwOTWWwsFVlNjXIupDW9MnEoiir9PeX6hosC1RT\n1FzSVq7R4uGcsZGrBfNVAK0Cg0fS2Y6U7P1AZWFtW0VdM10NVdyQS0rYP57EowvFDubLDeQhSECK\nzX4yb0lEQyCGuU/Tr4qq+zL8at9u0OPT0E08fq6IuCRy/eZYVMR102l87eQWPvv9VXziyeWe16yU\nFFwsKsxYI78Owd0IVDATtiBgQUbMc/xeQ3JUxNRQDIsB3XaLDMMSGtrjp2iLnfZr58NfOIV/fnYN\nS0UFNzQLuT/99iIePccnd2yHZVkdTsh+kItHsVVjR3zQ4MTX+HB9JnCkpQEKZifj3scYx0Qmho8+\ncD3ikohnLro3B4+uVPCRh87gU99bQSwq4qa5Iec+Ss7ljT6MWux83mAMs4Vgz4RiXYdhtWSATg4z\nhWG2mRNWseHBMMvsJqbXQmkmZzd6V4r0Z9H///AZvPEPH3X++/e/dgZ//q0F/MRfPuFcu2++cRq3\n7Mjhtt3DzusEQcBwgi0VBOyFfDbh/v3mhhNY3VaYLtMVlSHJJpE4jGNZZ7jPkoKbdd2wzHh4GGbb\nkNK9YCaMkteIUFnR0dBN5GsN10Xzl46sQTNMfP79d3RIS+PNpgZrsd/QTWiG5dp8EgShyfAGl2Sn\nYmzTr7KHJNsuxujbd95PaaqkYvb6wqQYv26rbAUUT04xwUNH1vDsUgn/7cunOs7JxUIdIykJ//yz\nd/awpDwuyvZYAmX/NJ3AWddhvwwzkWTT389uahSbxSxNhZD2MP9jqUSApjEp41nSfv6c6ZpHJtfW\nRqWBN14/jZ97xd6Ov9+1dwSmBTx6lq78JMUw7RzMxKPOrL4bSGOSxjA73juUc0QhiRFXJdlXARDW\n1H8RmOyTYT67ZS809ozxFcwAcNvOHJ44X8IP/+n3PLtS7YuhD718F26aG/JtNNQOu1Ppb/FL0I/p\n19GVCh4+uYWbm86ffpCUI4GPz189ehHfOlvAXC7OXQjevKP1HT/2+FKPg/pr/+hJvOGPn3SVmj70\nsy/FzfND2KppOL5WCVzo52sadW6RBnJMg88wG74zmAnmhuO4GJBhLja/r5+IpvbFSzdj++xyGY+e\nK3Q0lh5fKOHX/+UUlksqbtuVRToWwb8e28T7P82e+XODqpvQTYvqzMvCwak0yoqB8wGi7Ih/QLAZ\nZmLO479QJ+eT33EXKSLiuukMnllyZ5gJ2/8fX7sPn/zJG5GKRbFzJIEvfuBWPPzB2xGLik5Gr1/8\nYGkbXzu55cTS+QGPURMNTkRSqpNhZs0wVxs6fbFe15oz++7v92pieknxxlIypIjAZJi/8Nxax38/\nfs5eGJ5Yq+DYqn1sb9mZw6ffe1vPgs6erfOeD6WdW/MjCVgWqE7eumFC0ej5oqQQZs158xTcbDko\nveAmn8sqdmyG2d1fI8eZE0yUaJYFfOyxCz3f97GzeVw3O4T9LrE7Q828ZxocwyDKPop7nINFxnwt\nAKSakmYavPxp4pLILObIvs8wJOEAfQ1Yaja/6TPYzfd73C+WinX83Kefwdv+5HH8ySPn8Lm27ObF\nQg3zw+5rSMKss2Z4Kwrd+A6wnxlVRkGveswweylZGgxJNtA0ZuNRIVD2cSbOHs2oMZzyAWCYEX1p\nmlZHbFf3vPjNH/ka3vVXT2CzomLcxavohrksUrEIvn2G7i3knIM0hjkuMY8vKfaHGQwz0HJL70bt\nqunXVbQjqJGVI8kOOLNG8td2j/JJsgHgjt055/8vFekzeutdUrQdIwmMpaW+GOYg7ssE/Zh+PXxq\nC6YF/NL9u71f3IUUR5YmDSTC6/99/X7u99w0Z8sKXzKbQbGuU2PAnlnqZc6mhmLIxqM4slbHj/7V\n0/j3/3Dc93e2Zf51X+cU0DaPFnCGuVjXAl1DgO2UvRmwkdMddcKDduUZKbiPrVZQVXU88DfP4P2f\nPoJ/Pb4BUQA+8EM7kI5FcMeuHG6czeA1h8Yxy2EAR4PjyBlAkv2S5rn1NKWIZG+XnvfqhQRHtAcN\nTmRfgHNj50gcJagclAAAIABJREFUK82RiLObrbnx42sVPHnBzoV+zaEx7BtPOe+ZycYREQWMpSVs\nBsjaBoB3f+wHMC1/jvwETpET4JlAFmVjzYggxyWb0tnPxKOwLNZiXWPeq0nBTFMskEKGtn1RFDA1\nFGc6mrdflzPZeIfB0jdO2oZfNPbNa7ZO0QzUGgZVcj7XnOdcpPgjkOcCNYeZIxJnW9GpC1lS5LGa\nkHWGr4AXw2yYFjYqLIa56TTuwTC3Xye//aWT+J2HTsI07fznqqrjmYsl3LVn1PW9Q3GJ+fu85ku9\n2MOS5wwz2zSrrOiQo+4u6oCt3tCaOb5uIAU3rRnsxRATPxKaQzG5X3gxzJ94fLHjv9u/7WK+To1U\ndGT9jIKx0mAXjEmPpkRdM6n7F/BuijQME3KUTkh4qQCKHkqalBxlkiY2w0y/T7IY5o8+eh7v/LPv\nOv/96ScvOqMNpJH59GIJimZ2qDMIpIiIG2azTCdz74KZPZrTit5zPwe91n4sU7UrFVcLZga8FhY0\n9Gv6daGgIBOP+GLHfmjvMH6lWTi6sZSn1qs4vVHtWSTsHElgJBm8MAFai1+/bBHAl/lJQ12zu/DB\nVADBme3lkoI7duWceUoe3LtvBJ/4tzfiIz98AEDnDGb7wnR1u3XsXn1oDL/3toMAOgsLv1m0gO3q\nXWsYHUUED6SIiKQcCTzDnK9qHVJnPyAZ0EHgFMw+CrL2BVqpruHYagU/9tGn8cHPH3P+/ckL2xhO\nSviZu+fx9Q/ejj/98evwsX9zI66ZSOF33nIAmXgkkKyaMI9BGObdowkMxaPUuV4Was1CxW+uMNAW\n/xLAg8BxYA1w7Y6mZBRqGh45ncdb//wp/Muz6zAtCx/4zBF85qkVpGMR6iJiPCX3JckG+M0V29FP\nckI3wyyJ9mObNt/nVVCVahq10ADsxb5l0Y+rwzAzzpnJoRh19hUAzm/Z+/BHb53FTNPgb3IoBiki\nOBnB1LgTD/dWcu1nKS7ZhHVbpIwvkf1GK+Z4GOZthqcH2fesgrXGmJWPSbbhEO34FmoNmBZcmSug\nTZLt0iTXDRMf/uejuJCv9ZjjLRfreOufPIZ3/OnjOLVegW5auHHOXd2VTUSZTVZCJrDmvJlz9KqB\nqChQF+texZDXeBKRAtNk+8RpnvYZKQ+GmKyZvCTZrN9gWRa++Nxqx78R1YBumFguKZgfYRfMLBVA\nVdWZnhqpGJth9pph9nLJVnkk2R6y/aQcod4nEx6kCc2UjoA1w9wupf7Imw+jUNPwvx6/AKD3uifu\n2N0YTkrMaLaK07ShxUpFoeomw3BMc36HG1qmnpTnwFWG+SraEXQuV4qIkCNC4II5X2tgPOUe+E6D\nIAh43bXjADqLLoJ3/OX38fa/+D6+c7bYwZBMD8UwlpZQVgz8xXcW8cHPHfX9fbc9Ol0seN00WVA0\nM9D8sr3d4JLs5ZLKZfbVjetmMpjNxTCelvHE+RI+9Lmj+P7FbRQoxeibrpvA/QfGAHTOKwaJLzq9\nYS9Q943zy/wJsv0UrnWN26m6G0PNWUxal5+FYl1DJkaXnbqhXcL6Px5ewB998zwA4InzJewYjuP+\nAzabMpqSIAhCz2fvHk3iXbfOBPrOLTdx/w8fURBwzUQK57b8O4p7MT0skGsvyAyz02QL0EwZT8sw\nLXs0ArAN+J5brjgqmSmGc/38cAIn16pMsxsayLEJMs9P3hvEmZ/8rtFkJ8PsWTBTvuc2I7uz47tS\nFsM8Zi+jKZnK5JcVHVvVBn75VfvxW2++1pEOz+YSODSdce41NCnlcJLNMBPmhNaMmcjYhTnNgd/L\n2Z9nhrnCWDtk4lEIAjunuO4xP8mav6w617T79nMMhvnoShmf/O5F/NLnnu1peDxyagtHV8o4tlrG\nyTVbYkqM2brByzDTfmPKQ65rRxbR94/XDHFZ0ZijQnFHPeP+HSoeLtspj2uoxTD7l2RrholPfncR\nZzdruFhUcOvOlrqQkCVLRQWGaVEl2VwzzKrhwTCzC05VN5nzrQmPY6zpJjUJALDvf6ys7mJdY3qY\neJ1jNVVnzzCnZFRV95zydpLg9j0juGk+i282o1s3uxpR17iMNAA2M85qOnk5vbdUBO6f4UiyKeeg\n7PF8vxordRUdCCrJBpqS34CuxltVjepcx8JwUkJUFLDK6Ox/5qkV3Lkrh//5jsN4393ziIiCk6n3\nB984j6+fyjPNUNzQyusLbvoVxLAoiEM2gX2zD2BWpBnI17SO3Ek/EAQBt8wP4cvHN/HwqTx+84un\nnczMn7pzDkDLebv9YUrmAXePJmwTIJ8F2emm6USQgnkoETy2KF8Ldi4D7ayc/20XarrvYuyX79+D\n1zebTsW6jkdOt8y79o4lsbfpKcBqog1xLETcQCJQghSuAJCJs01uaOgnGqK1qHz+ZpiBFtP6/Saj\nXlUNfPNMq6OvM5xlX3NoDCVFxzdO+4vRsyzL9mmIR/HRB673/Z2Jm24QSfZWVYMUEZBpMqZRDkk2\na1vVhs48z9JxtmSYnDOsgnksHcNW1f05RNhl4o5OXLXH0jJunLUZy1QsQmWXcgkZxZpGXciVHPbP\n/ftFRAHT2XhHdFU7nGvRYwaZJpe1TTA1ZCgMc0QUkIlFmV4jRD1FA8sJXfE4PplYFBFR6Ii0cb57\n83/Lio6NSgNSxL1p/83TW4iKApXBHMvIOLtZxXOUMRGvgtnLkEnRDGbyglNwUgqiWsOgzk8D7U7m\nFIbZIQnohlJkO24oeTSFUgxJ9mNn8/jwPx/D7zx0AgDwnrt2On9b2yYRh3ZDg6YoazHMwUy/7O8Y\npf4+y7JQ1wyqkz9gr/2Ypl+GCZly/gH2MWKtVYu1hoeSJspkyD0l2Sm6U3g7qTGaknHv/jE8t7yN\nzYrqGId+4qduxXP/6X4cmnZXKmabTSfa2phsl/YbyblJO8aFms3A054jZH6cyjA3bJUHSwVwpeHF\n80t9wjAtlFXDt0kSQTIWnMHMVzXqXAELoiBgckjGXz+2hD/79gVntq+bTXn1oTG8bP8IPnCvfaMd\n7Spozns4bXcjaF41AExkbLZowec2Ae8uMwtBZ5gJez9NMVThwT17R5yFiRwVnK7wfdeM4ru/chd+\n/NYZAJ1y4jubTrEv3z8KC97zZ91YyNcxnpZ95yED9nENwqrVNQOKZvbFMAPshzoN+Vqj57z2wtxw\nHL/9pgOuf9szlnRM+MqMRljQ7xw0r5ogE2NLEKnb5Sh+aCDXXj8Mc5AZ5rEuCdl6WcUjp/MYb874\nssZL7twzjImMjH//98fx1ROb3Nusa7a07T13zgUyGUw7Rk/BJNkjSclRHEUjAqKi4Br3ArQ1MiiL\nUa8s6SGPgtspdhj33tG0jFJdd11sPXrOZlqumbSZFaIIGE/HcENT4ssa47h9zzB008I/PbPi+nfS\n3MswCiJWBqsjyQ4YK1VtGDAt9rntNYddVeku2QC7oCRSSVpBKYoCsomoq1KJFOGqZuDsRhUz2QTe\neP2Uk61NZmK/fnID88MJqoLnZ1++B3FJxB9946zr359eLDq/ww0pj4K53mDPx7IKTsDbUKqlnnE/\nxtt1HaJAb3B6GbttKwakCD2ylKgD3M4xMpL1jZObmMjE8MqDE/j5V+zBoakMVpumh8SVeS+lYHZM\nvzwZZg/TL8rvaxhWM6e6jxlmD0l2LOolyabHdgH2M08zLKraqNrQmU0VskZ3azytbreacSk5glcc\nsBvxX3xuDZvN9d54OsY8h4cSUeimRb3PnFyvYDQlU3+jl4qgVNeYqtCYF8Ps0dS7EhFKwSwIwmsF\nQTghCMJpQRB+NYzPvNwo97GgA4iDYHA346BFBpGw/OEjF/DRx2zJIokqunXHEH7/HYfwusPjHe8h\nhlQEZ7f8zegRyWAQeeXL949AAPDQUf7FK4GimUgE7G55yYloWCrx5y/TcNeeHMhSd7moYm3bPj6T\nQzHEoiJedWAUP33XHGZzraL8J26dxv9+8BAOTtoPQL+y7K2qhom0/yYMYCsHWPJBGlqxBQEL5kTw\ngnmzojnKiTDQzjCzpIY8nXs3ODPMAR9AKTkSqBjzYnpYiPdj+lXXIUeEQCMVo23ncTwq4shKBcfX\nqvjxW6Zx7VQav/EGuhlfVBTwV++6HoblbrBHg5PpGfB5QGTOQdQSW9XOczkiCoh5mOEA9IVOXWM3\nGoc85hvrmgFBYJufEYOy7hk/zTDxsUcv4Pbdw9jdlPO2M8w3NBnmYca1e8/eURyayuBTTyy6/p0w\nt6yCOSUzJM0ezauUxzw6jwlmLsmWLHsyzIzoMFJEsI4xYem7QeKSSoqOR89u4Z59o/jvP3ID3nTj\nNADg7n2jzjb2MPww5oeT2DeedmXRV7cV/N5XT+OefaM4OOXOrqUZDQ3AbpazGGavGWLbUIrBfnpE\ng5ZVm30VKU0rnhnm4SR97M6RdLvsA1KMmZbddBJFAb9w3z7cvCOHlW0Ff/3oefzZN89hPC1TRy+8\nxjaAZiQk4xxMMZq0KsfYRtLDv4bLJZslyebwagDooxU8M8wAXJ2y17ZVvPbaSXzufbdDEAQcnMrg\nhtkhfPK7i1hvSrJps8sERH1AW0scXy1Trx+gpdCjvV/V2JL5mMRuGtUZ0XdXKvoumAVBiAD4QwCv\nA3AYwI8LgnC438+93ChxPPRYSHqYTtCgGSa2Fd03O0ZAipRYVMSj5+wuLmEwH7xzHi/fP9pzk84m\nJOxpc08+S3FwpuG754vYN54MtOieyMRw0/wQHj5Ft8+nQdFNpuSHhWzCPj5+i2YStTHJmJP0wmhK\nxnvvnscdu3IoKTqeXS4jKgqOydt0No6ff9kuiG3HSRAEpGMRp/jM+yyYNysNZxHrF9mEFGhu2jGV\nCFgwkxt+kPzprWoDYwGvITfsHU9iZ1N++GBTOu+GwAyzhwzUC+lYFFWVLt+igcyjBinUEx6FGQvb\nir2Y8ZvPDXQqYm7flXOyul+2fwSffM9LnLl/GuaHE5jJxnxF6RVrbJMeLyTlCEQhOMPc/ptvnMng\ntnn6QsmLGVAYhlJAGzNBKehsOSw7W30sZd8ft7rmmM9sVLG6reKdN886/zbVVOuMZ2LYNZrEUDzK\nnD0UBAE378jhQt79OVXyMGQCSE4vbaFMDKkopltREQmJnjNMmos0ua793dgMs9cMcyZGj8RpMcws\nybzsPMvaQe5bZUVHXTPxiua19K7b5/GKA2P4v+7f57yWtVgH6KZQm+UGLAt44LZ5qkqCT5LNaoiw\nZ4i9irGERzFVrrMNYXlmmFnnOMv0q32UoJ1BnhqKoazo+MgXTqBU15nsZUQUkJIj1KaYbtiKGhbD\nnJTp1xBpotJ8FgC7ocNieO31HZuh9jL94imY3faxYVp2tBxTkk0K5s57nKIZKNY1HJ7OdJjiPXD7\nDpzeqOJ/PbaIuCR6+pWQZodbY0s3TJxar+LglPv8M9A+HuZ+jBWdbcrm2XhlRN9dqQhWDXbiNgCn\nLcs6CwCCIHwawJsB+HePGiCU+pixA+wbNsvJk4a841wXbGH23956EM8ul7FUVPCFoxswLavFYFJc\nMwHgz37ienzzTB5/9Z2LOLvJL4+uNQw8tbiNdzVlxEGwcySBb50peL+wC4pmMlkEFq6ZsB80p9ar\nuLGLYWeBRBUMB2SaCH723p14arGExxaK+PqpLcxkY9TFQztI8em3gN2sNnDdDP3m6rVNMjfN8x0J\nHIa5D5dswH/xqRkminW9g4n0g1t3ZHFirYJPv+cmpGIRPHxqCwcmUhAEAc/82j2X5Dv3K8lOxSIw\nLFs67KdxVWsYiAhgzorR0M8M81JRdSTUftG+UCZNjLlc3FEA8GA4Sc/QdEPLdT3YuSwIAlKxSOAZ\n5gOTrYXxm26YxMt20kdCPKV0DTbD7DX7Zr+f3agk1163wQ1Z7O8YbR2rQ9MZPHjXTtx3YByiKOAX\nX7nPNWqlHROZGEp13Xbi7fotpbo98x1nsPB2VnXV9W/EhJJ1HeWSMrXg9cr4BezzaKlIf87WPArC\ndCxKPZecGXPG+3eNJvGV4+s9/95eQMlREXfsHgFgNzX+7IGbAdjXTqGm4cG22Vk3JOUoampvU4MU\nwmnG/knHbMWMZVmujZm6ZnqYfrFniBu6ySzmCHNGc0LfVnRn1t/1/QxJtf1+g+oCD6DZkHJ/f7vc\nt71gfv31U9isNnDX3lG87+Pfx+3NMS4aMvEo/v7pZbz7jh3YMdJ573RiDj0K5lpDdz1Greg5dg4z\nYBeYbtJ++9r2Ypjd73GWZaHkYTjKYpgd5RUrh9khL1oFs2VZ+IXPPAOg1QgkeMtLpvGlI2v42okN\nzOXins3ibIKu9Dm3VUNDN9kMMzH9otwnFI/Yr5iHU3xN019Uhl9AOJLsWQDt2qiLzX97QYOcpP2Y\nfgVhmMkiLigrd8uOLH7yjjkcnk6johq4kFcchpnlHjuelvG2G6cwm2tlnPLgmaVtaIbVkQPtF0kp\nmDzabbHEiwPNgvn4mvuiiYZiXUNUpM8e+cH+5sOurBrci33SSPFTMOumhXw1uER5OCnBgn934H4l\n2V7mRTQ4ubUBf+9f/MR1+MaH7sDccBzDSQlvu3GKmwkNWjBXiIFGgMIVaGdU/F1HtSaTFYTplSIC\nRME/w2xZFk6sV52mVT8gD/Xbd+V8/YaRpIQtH9dQod5fwQw058x9MsymZaFQ83ftshY6pmlB1U2m\nlK41w0zP3/SS4rUK5k72hSz2p9syy6WIiF973QGnSH7X7Tvw6sOTzM+faD7L3FjSUl33VC+wZmQV\nZ7HPkjRLKFIiZcjagRYrBRAjRff9azSzjlnsTSbOwTAz3r97LIV8Vev5Du2qgsPTGdcF9d+9/w58\n81fuZTqtA3SfEKdgZs7HRh2Wzw2qRzGV8hiB4JVkUxlmlZ2g4tyPadFuCnu+VhAE6tjYahvDvG+8\n1QTfMZLEr7/+IO47MI5Hfvle/Kc3HKR+PmD7sZTqOn7noZM9f6s6x4g91mBS4ueIJJs9w2zvf1pK\niuIpGY7Q5cKaAd20PAt+wL0p4aUyAex7gCh0MswblQYebnpjXNsVOyoIAn737ddhLhd3suBZIPcP\n17GG5jlAy9kGvBufqs6+hogCg8Uwv5gipYBwGGZPrKyswDCCzfM+3zBNE8ViEStbTXfHRh3Fon/J\noQQDFVVDsVj09b4L6/ZcXcxq+H5vO+ZTNuvzlSPLOLVZR1ISodcrYDS1AQBDMnBuU+He9sKa/bpc\nxP9vJYhYGmoNA4VCwdeit6bqiFh6oO3GLQtDsQieWczjNXu9b14EG6UahuIRlEol39t0w1RGwmpZ\nw0xa9PwdhmHAMu0CfyVf5v7dW1UNFoCUGGxfxSz7gXB+dQviCL/Z2fKWfS6LWg3FIn8ThsBsPnTX\nCmUUi/zbPbdun+QJ9HcNBYHVfLisFyu+zueNUhVpWQx8XomGvX9XNgqQdP5xgXy5joTkfe7REIuK\nqDcMX+/fqmoo1DTMZ4Jv95M/sR9xScSTi7Yb7L07E74+KyNZOFZRud9DngcRPdi5DADxCFCo1H19\nz5KiQzctJIXOa9cwDKyurrq+hyibNvMFrK52LmjI4lBXa9T3W5aFiACsbJZcX1MoVyGJFvX9AGA0\nt7OwutXxHc4sbyEiAEalgNVasOYQAMi6zVweX1iB3OhsvKwXykhJAnRdp39HTUFFcf/7Rt6+Botb\nG6hQFDWJiIWNkvs+XFyz1VJqpYDVVXfZeNRsYLuuYWVlpeceQZpeBuMYRU0VpbqGpeWVHtXP+pa9\n/XJhC6sqJVoqYp/DT564gGunWvtvNd9ytZ5Oia7bJ59I+Wkt6CrKitbzGRfX7e9X2y5gVXT/ELNh\n//vZxWXX8bRyXcVwDNT9U2s2ZpbXt7Ca612/1VUNhqZS318tkftpHqurvQqafLmO6YzMvAYSURHr\n+W3X15QUHTEwzk8AaUnEUtc1aFkWVooKJtISyqqBHKpYXe29HwkAtuuAu0e5jbt3ZfDthTIsrdHz\nPc5v2ftPq1Wo39FQ7bXIwsUVDCc7z7OlNfv41avboP1EtWbfuxeXV2G6eMLUNR26Wmdsv456w/0+\nuNlsmlsN+jWkVJrbX13HWKTzPLxQ8P79gO3Ev7TROkanNuy1x0detxNDVhWrq72EzB+9bQ9Mk33/\nBIBGs9m4uLqF1ZHOc/Bic92tVIquxx+wm60CgJXNouu2yjUVw4lox9/a75kkiWWr6H4Ob9dUpGT3\ne0Q7gjTjLwfm5uijdgRhFMxLAObbt9v8NwfT09MhbOb5wcLCAnK5HPTmjXxucoTZCaRhOJNHpVFC\nNpv1dcKoF+yTf8fEMHI5/kKuGzdns7hlfgN/8G3bSfSdN00hl/NmgWdGCnj4DP/3VmDfdHZOjQbu\nNo0MVWBhA7HUkK/PaJjAUCrO9bvccGg6jYWC5uv9dWMFw0k58DZ7vsNUBqvlPA7PjXh+ZrFYRC6X\nw3AiipImcn+HFcU+RvPj2UDfe24cAC5Cj/jb13WriKgoYHZiJNBNM2tZiIoCNEHytd3Gpr1A2jE5\njFyOPWd3KSBHBDx0soRPPb2JP/7Ra3HDrLfkv6ItYzwTC3xeTQzbv1mIJX395pqxhJFU8PM5IUWg\nGvD1/iPNBf1Ldo0hl/PvOA0AZHN7psdw94FpzOX4GyoAMDVcRPFkCUPZbIdXAA0KiogIwOzECNfr\n3ZBNxqCYgq99tdX0k5jrunZLpRKmpqZc35NSNADHEEume16z1ZRIT4xkqe8HgEz8OMxIzPU1priE\nTBLM9wNAKnYcVUvueN22sYmJoThmZ/pbExwUUgDOQZdSPd9DtS5iNJNANBqlfsfJkSoaxibGxicQ\n7ZKDRmNlSBEBc4zvOJlbw4m1suvniwv2QnfP/DSGKbLb2XEFhrWO9PB4j1Ot45MxmqN+/x2TDVhY\nRzw72qM+kE43Ze+zU9QZ1JsjGQDnUbLiHdvQxZZM+7U3zHseYxbGhyuo65uYnJzsuP9L5+39s3tu\nimp8NLNmAVhCKjeCqdFeJYpmnUIu03vsHSQVACchJTKurzFwAtk0/f1CUgFwArLLNQQAinESY1nG\n9gGk4scBKd7zGsuysK3+ALNj7GtwfvQC8mrndZavNlDXTXzwnn34kVvmmC7HXvjL90zidf/z2xAl\nued7rDTsgmx2chRTU+Nub8f0iglg2T5GXZLuC4od2zc9PoqpqVH3928JABaRyo5gqktabFkWVP0H\nGMsNUffRaK6ChrGBiYnJHvO1WtMlfGZ8hPr+Ob0E4CwSmWzPb9ww7KbZzMQopqYmXN8PAGPpM6hb\nrfvMybLNLu+bm8TUlPt9nveKSuV0AMeBWLLnN0SX7KbartkpTDHqhHT8GMxo7zkIAAbOYCid6Pjb\n6upqx39Lkecgx3u3b7//LHJp9785rzEMxOPBE2UGDWFIsp8AsF8QhN2CIMgAfgzAP4XwuZcV/Zp+\nTQ3FoGim7/za5ZIKAXbcUj8QBQH/4x2H8aM3T+PefcP40Ct2cb1vPCVDMyzu752vaog3TVCCIsWQ\nxrCgaAZT8uOFg5NpnNqoUk0n3FCqa4Gdct1AZKl+5i8PT2fwAx8Ov0QWGXRmdCSg0Vih6fYetMMo\nCAIysYjvTGMSKxSm6Zcf5JISLhQUKJqJzzzlHn3Tjc0+JPNASzrnV/JbrOl9yYzjEn2OjIZTzcXM\nNQyXXV4IguC7WAaA0aQE3bS4z621bds0L2ixDJC5TH/nMjEm82MCKTPMWmoccl3AlvzS9k1d43NH\n3TGc7DHmWispzNEgXkxk7GO+5ibJVtiGTEBrxtVthMHLRRywr3HaWIxj+sWQg7YMDXs/w5GDMr4D\nGXMpuBjX1Tkk5fMjCURFAee6DD63FR3XzmTw9++/A6+/PnixDNhyVstFslvh8GtwXOUpho+ekmwn\nh5lh+sWaYfaQZCuaibjHNZSSo66S7GrDgGF6p4rMDydwsVBHQzdxZNnmir933i5kb5jN9lUsA7bx\n11BCcr0GyAgKj6TZ7ZnD49QuM0ZHyL2L9X5yD1NcZNmtaDiGSzeZU3f5/o6HkccxGknJHbFSxLMh\nqH9KO9KxCCKigJLLfcb5fQxTMsCuXypU0y/Tc/0sR0V6DrNmMH0ArkT0/Wsty9IB/ByAhwAcA/C3\nlmUd6fdzLzfKio50LIKoD5OjdpBF3MWi4vHKTpzP1zGdjQWezW3HUDyK/+c1e/EH77yW20yIOClv\ncDrI5msNjKSCF0VAy5zE7/ylbfwR/BQ+MJFCw7CwsMVvclas64Gjxtzw2sPjeNP1E9g3zl8w3zw/\nhDObNW5TuU1n0R2wYA4wNw00z42A88sEmQAZ0KRBEGaslB/85huuwUcfuB5vuXESXzm+xdUI2qo2\nMJYOvq9I06lCWSDSUPQwRvFCXIr4jpVaLqnIxCOhXkd+Qc7pLU7jr5VttWPuNggyMXoUEA3k+/k5\nl8kcvFvBrDTsf/MyaxlKRKkOujVOd9Sdo0lcaIsoPLK8je+czfeY4QRBNhGFHBWxtt37fC15xMkA\n7JgvLwdmwJ5fLNU1mGavXLei6khIYg9z3Q4yG+za1ODIRifMdXdsF2AXeXJUZBo0ShER45lYz/4r\nKzqycQnXcahivNBqSnTu44qqIyoKzMW2E3tEzZpmm34lPWaIVd1gu2R7ZG17OQwDdJfwKkcxBwCz\nuQTWyir+4z8dxVv++DGsbit47FwecUl08sr7he0W3/sdv3lqE3FJxKFpulqpZazmfg0BbJdsmWFO\nSApuVgoKKXjdmho8c/LJGH3dWeL0rBhJyR3mkWTtEdQ/pR2CIGAoHnU1F+Q9hzIxeuOzoXuvn+UI\nvSGuamyfhSsRobQHLMv6gmVZ11iWtdeyrI+E8ZmXG6U629TBC/0UzDs4DAEuFQgLuVXhW0gWanpg\nF2QCctH7YZg1w4RuWn0VzMSS34/x17bSHyPXjT1jSfzmG69xdYmk4eZ5ezHz/YusCaUWlksqRCG4\naoEsPv2D1kv0AAAgAElEQVS4CgPNc6PPgnk8LWOjzB//AwCLBQXjaZnJIFxK3LE7h5vns7hlfgiK\nbroaE7XDsiw79quPh6xXNiwNhT4VE0Hyn9fKKqY8XJAvNUgB+sjpPNfrV7YVzPTJjKYpi1MWiKHM\nqI/rSBAEqoMsN8PMWGgpmoGk5H3O7BxJYrFQh95U8Lznb74HAH03HgD7N05kYu6mX4ruWTC3cnJ7\nf2Pdg70EbIbZtNwLOq8MZaBVSLgZJtU5CmanielyT1Y4FQDjabnDlG2pWMfJtUrfzCUBzYiw0sww\nZjXZvQpmr6ZGRBSQkES6S7ZhMZ8P0YgIKSJQc4JVzXRid2igmXaRz/Q6RnPDCVgW8HffXwYArBQV\nPH4uj5t35EJj9lJytOc7GqaFh46s4WX7xxy3b/f30gtOcl4zTbsIw+yi8OOJRnOysl2OEVfBTFQE\nLu8nhn5e45gjKamDYd6qNJCQRGYclx+MpmTX+MNqw2Z3vdaNmYRENf1SNIPpkg2wnch5mkZXGl5c\nv9YHeGRdLMw2C+alEn/BbFkWzhfqTlTK5YDDMFf8Mcz9IIgkW+XI+fPCrpEE4lERx9cqXK+3LAvF\nuhbYOT0sHG4W+kTa6oXz+Tpms3FfRXk7oqKAXCLqm2Emkux+MJP159oO2L/3cl5DBI7s0qOgLKsG\nGobVl4wrE2MzKm7QDBNlxQjk0UCwezSBcwV/x2d1Ww1FltsPyPnx3x9ewJJHU9Mw7Wi+KRdjGj9I\nxaIoN6NyeLFZtV35/bLxMYqUTuFYiAK2wyqLYY7L3veSnaMJ6KaF5ZKCi4U6CjUNt+zI4cG72XFE\nvJga6i2YjabM3useTSKNaHJST4aZSKJd2B9F85Y6tiLZXBx6OQoqJsPMqbwaS8ewUVZxbMUe7/k3\nH30SFVX3rRihwXEh7trHVVX3ZMbSjEgc0iz3evanKIoOw7RgmBaTYSbf34291Mn2PfZxiuKK70QW\nechpu52UT29UcXK9gpvnw/FPAZppLl37+MxGBRuVBu476D677LyX8cxpOc17uzC7S7KJyzaHCsBl\nHzsxjYw1fEuS3fv9i7yS7Ga8HDHI2qio1Ln8IBjPxHqSBoBW08kLzManbnoWvLEo3YncK5bqSsTV\ngpmC7br3Q5eFpBzBSFLyXIy1I1/TUFGNy7rYJwzzJq8ku9p/UcQKkKeBFMz95MBFRAEz2RhWOQuy\numZCM6xQGeYgiEsRjKdlLBVVrJQUPLXIdlYOo4AcTkoBZ5j7ay5MZ+1FMe+cuWVZOLdVx+7Ry18w\nk4e115zsVggz1+Qa8sP2ks5zPwqRayZSyNd01y44DWvbKiYvc8E8NRTDf32rHbvyySeX8fmn6U6f\nm5UGdNPCdJ/fOROLQG/GOvFio9zAaEryPTst0xhmjoxegD7DvLatoKzoXAzmzmbW8vl8DY+fs5n8\n//zDhzAZgiQbsLOY17vu3aTAyjAinYC2EQYqw+xVMNvPSbexGK/8WKAVt+NWLNQ5CqqRJJ1hrnNI\nygFgPCPj+FoFb/qjR/Hlo+u4kLdHk166k53fywu6JNvwXOynGcfHYS89WPykSzEItPa5lwIpIUVc\n2cvWfC37/WnKDDOP5B7oLZi/fnIDlgUcnu5fLk+QikV71l0nmgSC13ZYqibSdGEVVKwZ5nrDe4Y5\n2SfDTMYW3IiaYq2BlBzxPEeGUzIsq8VIb1UaDukUBsbSMjZcVDQ8TSeA3Md77xFmM7rO6xymNV5J\nPOFVhvkqANiLyX4YZsCWZS8W+Atm8todw5fPVS4pR5CJR/DMkrfc17Is5Gta3wxzkIKZZPf1e8Em\n5Qg1B7AbZJYkG5JkrR/MZmO4WFTw4CeexXs+/iy1oLQsK5SCeSIjczcWAPshWG0YITDMMZgWsMYh\ny27oJv7vfzyBbUXHroFgmPmK2DBmrqWIiHhU9HUNkQZIro+mxoFJ27jr5Dqf2kHRDBTq+mWXZANw\nmioff2IZv/HF0zApzO9y87yf6ZNhTgeQza9sK4EkzPZCx0UOylkwJ+WIq1z4Q3/7A9Q0g2vGdf9E\nGqIAPLFQwHfO5DGclLB/Iu35Pl64SbJJke9VkKUYkl9b7ushyU7QM1IVDrluS5JNP0asxIiYFEFK\njnTIQVvb9y74AXQwYV94zm4Y/eprr8HP/NAuz/fygJZzy8OOOdeKS9OGJycbaJpuuczXEgmwVzFE\nY5hb6jaPayhGkWRzHF/AVlftGWsZI371+AYAMOeK/SLlYkR4YrWCqCh0bNsNQ07Or3vTCGCvzxyG\n2WXtQpQXrIKOlZXNUzALgkBtipTq3j4IQJsharNxtVlphMswp2PYrKg9qiRehnkoHkXZ5R7Hew7T\nGq88pmxXIq4WzBSUFN25IQTF/okkjq1VqAuxbjhMU4gdqiB44KWzePhk3pO5LKsGNMPq29gpxbjx\n0cD70PRCgjJn5IaWc+LlL5hncnEslxQsN/MiT62751luVBqoayZ2+MhPdsP8cMLXPD6Rb4chyQaA\nZY7RhlMbVTx0zI512DUADHNLkk1nmKuqjl/7p5MA+r/uh5MS9ygF0GLH+lFMHJjwVzCTxsfU0OW9\nxwG9rvHnKOZ/hHnrd/aWZTRFw8q2Gmh2mjZ7VuecYaYtJFe3Vbzx+im87aZZz+8wnJRxx54RfPZ7\nS/jXY2t49eGJnviXfjAxFEO1YXQw4eT/e83hphiGPzwMsyOpdtlHiu7NMMec9/ceoyonAzmclFx9\nJeqcpmzt5//XTtjF2DWT6dByU50ZV7WbYfZe7MtRe4bYdT6WQ64L2MfY7dnOzTBT1gZOseC1fTni\nasLIq/IQBAGf/Klb8eBdOzGXi8MwLWTiUWfcLwyk5Cg0w+pgEU+slbFnLOW5f1guzsWaBikiMJsC\nbNMv7/Ud8SGgFcxShG0sZ3+G+zEu1DSuUaWRFBlhVFFVdSzka9gRYrN+LCOjrpk9TdaqanDNSaeb\nSqHugpunIQEwRns433+l4cX1azlhWXasUr+F0U1zQygrBk5vuBcz3SjU7JtrvwVov3j3S2cAAN84\nlaea4liWhQ9+7igAYJ9HJ9ILQRjmVmxBnwwzZWHoBlIwX25JNgDMZuNY3VYdwyaaIuDspr3Y75dh\n3jEcR7GuY5s3biykgpnIYFdK3ux2u7nFoanwmKygyDDm8AievLCNjUoDw0kJs30ymPPDcVz0oWgh\nc1r93G+yCQlpWeQ6PgAclcLllmQDdvedOEoDwDMUE70vH9/EZEbuW7Xgl2HuZ3ZajkbYBbPHfTMh\nRdDQTWc2j6BQaziLRB688fppbFYaUDQT77jZu8j2AxIt1c4yl1X7vuOlDiPHwtX0q+E9wxxjRHep\nHg7OQEuSzT5G7M8YScmUGWY+SXY7E0aKht0umcdBQRb0/+Efj2KlreFZ5SiYBUFAOhZ1VwBwOr3b\nsU4uBTNhmD1mmBNSxNUQiscBGrB/f63R61ngmH5xNDVG0zH82usOOPvrJXPZ0Boa9nfsVQGcWq/g\nmknv56cgCMgm3F2cNysqxtMx5neNMSTZPLFUzgwzRZLNNeMbj7qOVdjxod7PxQNTGcQlEZ/93hIe\nObWJhm7iFQfYs99+MN68RklcFYH9+7zPn6G4bU7Yvbbm2b8AvfHK+/4rDVcLZhfUNdvUoV/p7U3z\ntvW/F1NLUHAkkpe3IEvFohhPy/jrx5fw85896jqf+OxyGU8tbuNDr9iFu/b0Z0IRqGDmnCPi2TYv\ns+1IsgeAYZ7N2VJlUvS45TKbloVHFwqIigKun+lPxjXfnKdaLPJFcJFzud/mDzGH4jH+IgXz5376\npssWKdWOhCQiIgBlSpYoAFxs7s/P//RNfT985ocTWCzwR6SFdb+x84X5riGiUpgJwSm5XwiC0MHq\nP7XYWzDnaxq+c7aA1187wYzp4UGaU6JP0M/sdL8MMzH1ai8YGrqJqmr4ahi+/aYZ/OabDuNXXr0f\nN4YUhUMw2ZT1r5dbxRg3wywz5i81wzNjN8ZixzgYZvJ3N0OdGqdkNx13LyjrmunZEAFsQ6FuhHld\nku9fVnT84dfPOv9eUQ2u+ctULOoqyXZypr1ykCmu9LwMs6ckm2OswbJ6XZh5Z5jb8fJmEfbhNx7i\nfg8Pkl151aZpYaWkOM97L+QSkmsTfaPSwJhHKoczw+wmyeaQdCcokn/Azu/mYWBncvGOZg5BkSOa\nDrDHqN59+w78y7Or+IXP/AC5hIRbdoRnykaaWhtdBXNVNTwzmIHWfbDbj4LHVA2gS7J5js+ViMu/\n8h9AkIV3vzPMs9kYRpISjq7yuTAX6xqScmQgwsDncnFH3rlcUnsKkC8e3YAcEfCOl0z13fEkmYyB\nJNkeMxheSMr06Ilu8IbZPx/ofqCdy/eqGH7so0/jxFoVt8wPcedw07dnL6QuFBRcyzFDFVYeoRwV\nkZBET+MsILzrNiwQloQlyV4sKEg1DQL7xY7hOAp1HWVF54qGWd1WERWFvlUA6ViE+RvbsbBVRywq\n9j0PHBbGUjKWSypScgSPLRRhWVbH/ez4agWGBdyzt38jJL+S7JU+ZqdpUjpyj/W6bxJTL0VrGTSR\ned1hH9e0KAr4sZfOcb/eD4hKod34q9JeMDM8CqWIgIgoQKVIqr0KTi92zGv/esVKeeUoA3bRv+Uy\ngsE7w0yeY688OI77Do7jQr4eqmS+3bSM7GfLslBWNK7nEZVh5pZk90YmAW0Fc4T9WxNSBMuN3mKK\nxwGabB+wi5v2feGHYSb40P378L57d/f9HO9GusvpuqLqMC3+JupQQnJnmMsqZj2KbpZLtsNgMvYR\nbUYesBsAPPtqJpfAsy7qvGKdT5IN2Mdm73gKx1bLuH42y8xf9wsSBfpLn30WD33w7o4GB8sBnMAZ\nC1O0jpGiVs619wxzUAXAlYjBWFkOGAjz4jfKoxuCIGA0JTmFlhfyIbgKh4X54biT87tcUjoYyosF\nBX/3zBpecc1oaJmNSTnii2GuObKs/m5O9qwen+lXSSEzn5f/GO0ZS3b899p258Kpquo40cyXftn+\nkb63R3LFyTynF8isatDs53bQ5oy6MWgFMwCk4xFqsW9ZFhYLCuaG46HI7EhT42JR4ZKkLxYVzGRj\niPbLnMp8DQ0A9ozXcNy36/OlAmGY33rjJD7+xDLObtWxt+3aIoz4fAhGjGmOmfZ2EJl7kAguOep+\nTEjkkFdhRBZC7ewYMZjqx1U9TExkyLhGG8NMXLJjUWiMglkQBMQlkSK59ZZke81fes8wsyXZKY5i\nKiG5PzN5cqABYO94Cr/7tutw/8FxDF2CY9re+D+7aT+Ljq6UUddM7Jvwln6nXQypAL6MX8BeU7jN\nEKt+GGaGS7YXsZF2lHM6xtEmf1f5JPftiIhC6MUy0FnUA+0qOr7zIZuQXJs2G5UGbvSIv2JeQz5i\npWgzzDyS5ZlsHIWa1jH3b5oWtyQbsH/H20MeNyEgRe7qtorHzhVwX1NpUFF1rntEJuHOMPOOFVAl\n2VdnmK+C4HyzKJgPwVxhiBLP4YZCTRuYxUi7VLF7PvGjj18EAPzifbtD217KZ8FMTKD6nYUksiue\nbNRSXUdSjgTOMw4T7YzkntEE8jWtoxO42Fzov/eueTzw0v5v5gkpguFElNtUar2sYigeDaUDydtM\n2VZso49BkgllGAzzT378WXzrbCGU+wwA7Gh29M9zNjUWC3WnEdIP0rI/hnkQDNkIxtsKZgD42omt\njr8vFRXIEaHHICwISHNxm/N5QHwAgowXUCXZDb75VrfFqGPk12cqQlhIxaLYM5bCt8+0jhmRh6Y5\nDDuTUm9CgmVZXKZfXgyzF3MTdxhmd0k21zGiSIYrqs7NXr71pplLUiwT/MO/uwNvu2kGp9YrME0L\nX3huFVFRwCsPTXi+l5ajTH6zZw6y7D5DzOuS7W365VWwu2d91zQDclPhcLnhxKs1GwtFn2M6OReG\nWTdM5GsNz3tmRLT3gRaQwSTGcG5NDd4ZZjKC0N5088uyX0pk4hI+897bAMBpTOiGCUUzuSTnhGHu\nfubwRqPRcpjVFynDPDgrywHC2c0aRAHYNZr0frEHhuJRrgXSqfUqzmzW+pZHhoX2ztNyV8G8XFSw\ndywZiPmgwZ434i+YF/J1jCSlvtnEpByBhdZMNAvFujYQ7DIBedwSNnG9rZglEWWvPDga2oN5OCk5\ni2YvrFcamAyBXQbobqfdKDej4MI0RekX6eaib62s4n2fes7xM7AsC083FRxeCzdekOtxzSW3sRuW\nZeFigX9WjQXeGWbNMLFUVLBrpP/7alh47eExvPu2GewbT+GePcP4m8cvoljTUKpr+I0vnsZfP76E\n2Vw4jHgmFkFcErHOEZEGtNhdHiahG9RYKU5DKCeypa2gdGbeE5ffH4Dg9ddN4rsLBSertKzqkKMi\n11hTXIr0FKwN3YRlccz2RegzyIpueL4/GrEl19SmBscxT7kwoLWGjqpqOGZBlxvXzgzhlh051DUT\ni8U6vvDcGu7aO4LhpPc5RJNkk2eB1wxnKuY+Q9ySZPdn+uV13245sXf+BrtpNRhLb1J0HV22PVDI\n2AUvu5pNSD3RavmaBssCxjiiA2NRkT3D7HGvoqks+GeY7effUqnVZPbLsl9qHGyu7/JNLyHye/li\npezfQGOYeUZHWF4YgzA++nzixfVrOXFms4a5XDyUkyHDWTC/4y+/j/VyY2AK5gdeOoMH75zD7tFE\nT6TPZrXR92xqN3gbCwTnQmKqyKweT0EWhnN6mCAd0MOkYG4rlC40zZ92hFAQtW/PbV7JDevlhiOZ\n7Bd2niYfwzxIcmwAyDQl2T//2aN4bKGIb54pAOjs+BJ2s1+kYxFERcFx22ehWNdRVo1QpMYpmS47\nb8dySYVhXd6c+W7cPJ/FL9+/BwDw3rvnUVYNPHmhhN/+17P4/NN2Nu1sSEZIgiBgeijGnWde1wxE\nRQGSx6ylG1gMMw8rkGibYSYoDhjDDAD3H5yAadlZz18+uo6/+NaCw6p4wa0gqnPMTgL2bLYUEdDQ\ne5VJPC7ZgF2Uux2jWsPgYogJA2q2OZlvNJsx4yE1K8MAmWX96rF1XCzU8brrprjeRyuYSQHqJblt\nxVp1Fcw+cpgVzezYvwB/Bm233Jmgpnk3VJ4vkKL+v375FB4/l28zguQ7f3IJCWVF73DTJ80rHlWO\nHKHPyAoCx5w5RWVhzzBzSLKbCquVtshMvyz7pUZSjiIpRxyn7JIP81nH9EvtLpjJDDNHrBSjoeFn\nrOBKwGBctQOGs5u1jjm2fmAHh7MX+1rbCdnvPGFYSMei+ODLd2H3aLKHYd6oaKFnRWfjUZR8FMzn\nt2p9x7wArRnoeoOHYdaR7TObO0y8/555AMANs/Z8efsc82JBwVhK8mUs4gWbYeY7RmtlNZT5ZYBf\nrj+QBXMsilMbNWeenCwOiLT9d958ALftDMdVUxAEbhUAUSCEMpsri6iohmfePGnoDEKklBsIQ7+t\n6jiy0nKd95NtzbMN3oK51jCQkMVAiglWrBRvMUa+A8GgzTADdhYzABTqGj7wqacBgMuBGbCdwLsX\n27yRTgBxkO18v2bYCRs8zfaYJLpKsqsNPkk1eY3S9h2Im66bA/blAmErP/XERUgRAa/ikGMDdkHs\n5nJNjN28GDanYO1ieBucM8gtlQUtkse74AZ6m/F202owlt6z2QR+6u6dAIB/fGaljWHme45mm0Xl\nttJ65pDCboxD5SAzCrKEFPG89yUl94K51uBzkSZO+2tt92S/LPvzgdGU7KTVOKMxHE0Nsh4qdxEd\nCudYAc30i7dpdKVhMK7aAYJuWrhQULA7pII5E7fnaDSXmwJBvm2Bq5ves7TPJ2ayMSyXFGcOSDNM\nFGoaxtPh3kyG4lFsc7KXxZqGQl0Ph2EmDzWOLOZSXRsohvnHbpnB0796t9PcaZfint2shSK3bUcu\nwVeMaYaJfFXDREhNlZQccYxSWBjEgrl91jwqCg6zvFW192PYjacRzoKZ5LeGoRRJx+yxBi9ZNik8\nw2qkhI1Ms9DKVzVcLCp4920zGE5E8d6750Pbhh+GudYwHQWMX7CkdDxyX9JI7GaYk3LEcz73+QSJ\nfmx/dvDuXzeGmVcKCrjvYz/usbFoxHUUqKIaXHJL8uxqLxgIuzcxIJJsAE5u98JWDXvH09xS13Qs\nakd8dq2dKqqtvPBiiJ3c865YP15JNrn2us8R0iTxNP2iZH0PkiRbFAX86msP4C03TuOhI2vO+cNt\n+hXvdNAHWs8BHqWHHBGcedh2KLrB1XRKyBHUtM79a5qWXTBzNM6iERHpWNQxdAVaBemgSLIBYDQt\nOzPMTuOSgwGPSRFIEaGHYVad+5x3rJRmWB0KAoCfob7S8OL6tRyoNgzophWa5Jgs4FmLSRLBc/+B\nUXzoFbtC2W5YmMnGUNdMJ++3HyMaFoYStiSbx3zr9KYdobQnhBlzVpZfN0p1faC6joDNKqZiUaRj\nEaxsq3huuYyf/uSzeGapjFt2DIW6reFkFKW65skkblU1WEBokuxkjJ9hDsu1PSw8cNssDk2l8Ir9\nI9g7nkSprmOtrOKhY5sAwilY2zHMKZt35gA52TgWiPTRS5ZNCuYwDLQuBZJyBBEBeG6lDNOyRx2+\n/qE78KqDY6FtY2oohs2q5tq170aNc97YDTRmwJ5h5pvvJa8nKNQaA3f/i0l2DGP7gp1n3wJkhrnz\ntU7GLw9DHI30sGOkmOLax1ERDZdioaLqXMUGOTfa741ExTGIDDPgr1nWYog77/0kMsiLfcw4a68u\nhtmH6RfQuzZouWRzSsJdvv+gFMwErz48iW1Fx8MnNpCORbmjkYhhXHsSjJ+cadnlGgKImz+fyqKb\nYSb7O8nBMAM2m15qazI78XkDIskG7HWCwzBX/cX7uY2FkkadV/OzlTfvrsTpN9b1hYbBumoHABXV\nPpEyISwkgVbBzJrPJQvJB++YC70Q7RfERXC5pODkehVLzVmPsBe92biEhmFxmW+RXOvD097ROV5g\nZfm1QzctbCuDNcPcjp3DCSxs1fG331/BE+dtY6m79/SfHduO4YQEw+rt2HeDdGhHQpp19BMrNWgM\n83haxqffcxN+7+2HnLGDf/fpI8587FjISg1eSTaJZQtDsp+J2Y8Rr3zhjUoDcUkMZGL1fEAQBGTi\nUfxgyZZj7w6hIdeNaR/GbHXOWVY3EPazuwHJ65Ltxl5uVhqhKyLCwFA8ilJd9z3r7cYwtxpJPOyY\n2MOO8eabktcoLqZhVZUvY9WdYW4gKgoD1dhIyBFn4c0j0yVwGOKu+0pF4ZtPdWLclG5Jtn1NeBbM\nlNgiv5Ls7u9fH6AZZoI7944gKgo4vlbxNbtLGq61Ntm7M9bAVTBTJL+cjb2ky9qASPB5m8FDCamD\nYS46sbKDcw2NpmVsVhpYLtZxZMU2Cx3hzcqOSz3XADmnvfZxitY00vgbg1cSXly/lgOOA11IC2+e\ngnmzYl+gg8i8zGTtB9z3Frfxzr/8Pt7z8WcBhC8lJYXoNkdm9bGVCiYycijNhWTzgveSZH/2qRVY\nAK6bzjBfd7mwZyyJs1s1tK+Pb5gNl2EmD9K8R0FGus3ZkK6hpGxLF1njCoZpoaIYA1cwE4iC4Iwd\nnGkqJIBgDsgsDCejXAWzs6gIYfskb9TLq2Gj3MBEWh4oF/NuZGJRRy6/MwSPhG5MNe+n3b4QbrDN\nn4I9op3YI6OrYObIGAbaGebWYnaz0nDktYOEoYSEpWIdmmHhNYcn8KVfuJvrfQmXGWIin+W5Ltwl\n2X4YarGH4bYsC2WFLxLHbZxovaJiLC175mw/3yBsnR/3blIUV7rWTtUGnwMyreAmbJmnJJsyrqXq\nBkTB228mGrHd2t1mmAet0EjHoril6aXhFvNEQ8olOosUYzzXkBwVXBnmqmpwMcRJF0NQMr7FM8MM\n2AqIdoa8WNeQjkUHIj6UYDQVQ6HWwC997ln8zaMXEPWRy52JR3uuoVrDgCB4ezWkaS7betOUbcAa\nP5caL65fy4FqI1yG2XGpYxbMNsMcFiMXJkhw+rfO5Dv+fSzk7+rMwnAYfx1drTjO0P3CrUvfDdOy\n8OffWcRtO7O4d1+4rG1Y2DOWxHq5gZPrVRyYSOFLH7g1dAM5sujxkvySbu1QSGw8rcvZjnzNloGH\nLXEOE9mEzTC3s4ZhF4/DSQllle2ZALTJ5kKYRyULWx5J9iA2BdtBGi5hG+YREL+Bk+tVz9f2I8l2\npHTdLtCckUVu7NpWteGLIXy+kE1IOLdp789XHZ7E3vEU1/viLgwzWfjzsFMxqZcd82OGE5d6TcPU\nZmOQr2C2X1PrYJjV0EZhwgRxXR7zIclOUyTVvBm7tPf7lWR3rw2ICzrPvTvlYlxWbQwewwwAH3iZ\nnRZwvY9Geys6q7WPqg0dgsAXOURzya42dK5rMJvo9b7x2wzujsYq1gYrPhSwXe9NC3jyfBGATV7w\nrh1sSXbnPqprBpIc5zCtflE0e8Z8kJvflwKDd9VeZlR8ZJzxgEuSXW1gODFYHS2CoXgUmVgEjy/Y\nMt8PvnwXbpobCn1GKuNiHuEGzTBxPl/Hfs5FkRfIovj/+8o5R4rTjeeWK9iqanjLDZMDe4PYM2az\nYcfXqtg7nnQaHWGCuON6FszNbm1YskCegpnM7g2qoRRgS6O2qprzO2Zz4S9sSVPDi2WuNp1aw8jo\nJs1Fr/PihVAwk/vQpXLyHk3JmMzIzlgJC7WGGbhoz8Tdr1WFswiPNE2VSEFpmhby1cGVZBOjLz+q\no4TLDHNLzsknye5mxwjDzGOGE3dxMifFXVBJ9un1CuYHKOecgMhH/TQ0qZJsla+Y8pRke5p+2e/v\nkWRzGlIBdmZ5+71Y1U1slFVMZgaPHLlr7yge/9WX47ffeh33e1pNmzZJdoOvGAOI07y78R0PQ5xN\n2J4d7aMnraYX3xp+qKtgLtU17lit5wv7JzoJom4TLhYysWjPNVDjdOInXgpusVQvtvll4GrB3AM/\nDvRid84AACAASURBVH884JNkD+ZChIDMMWdiEbznjln89btvCJ29JJJsL4a5UCOGUuHsL7J4LCk6\nvnpyy/U1j5zOQxSAe/YOJrsMAPvGWg0EIqMPGyTb80JBYb6OFMxhyaPJjZ1l/LVeHmwHZqAzN/H3\n3nYQ//DeW0LfBmlqeBXMtYYZmhx8PCUhKgpOVJUbLMvC+oDf5wA7NxtoxY1cChyaSuMYV8EcfIbZ\nyRcttY6JZVk2a835me2S5ZKiQTctjA7g8Wt3s/VTMBOGuWOx3eCXc7pJsp0cZ47FpBztlYSXOSOT\ngNazizTgNisqVrfV0EdxwgAZ5/HTaKcVvFVOhjkWFSFHxV6GuXnMvGbeE1RJtsldMI9nZMd5GgAW\n8zWYFjB3iZ7R/WIkJfuaYXaeze2SbB/3GFrOb5WzKZJNSNAMq0Mp4oxVcKpEc82CmdwHinVtoByy\nAeDQVOcoIM/YFUHGZYaZV/JOZZh140XnkA1cLZh7QCTZPKYSPCBSYxb7sjHgC8nbdmUB2DeWS8Ww\ntuJB2AVzPmRDqYQk4oGXzgAATm+4yySPrJRxzURq4G6i7ZjNxZzF/vRQ+OwyYM/Y7x9P4uuUxgJB\nSdEQl0TuRYUXUi4P5W4MugMz0DnTfXAyfUnmf1oMM/s6snMqw7nHRSMC5ofjOLdVp75mqahC0Uzs\nCSmu71KBNEqnhi7deXRoKo2FrbqnkR2RzQUBuQesthXMDd2EZfHL8BNSBOfzNSia4YwNDeLIQ7s5\nj5/naEISYVmdrtrkHsPTqHDLYeaNayGvoTLMHOuPZKyzYH5u2TYDum6AC2Y/kn4y79ydg84buwXY\n+7GbHSurdpqCZ8YvRZKtcDo4A/ZvWG8rmBe2bP+K+UugLrocSDpNm06XbN57DE2SXeOcU8+5uHT7\naXoB9uhYe9GdrzZ8NQ2eD/RjQDYUj/ZcA7zNWGrB3Efk4QsZVwvmLoRt+iVHRQzFW0YybtgccKni\n6w6PA+Bzdg0KUoyymHjAzkgF+B0CvSAIAn7llXtww0wGJ9drrq8pKfrAuZd3QxAEPHDrLIBLy7Le\nf2AUTy1uOxEHbgg7fivVtTB0w3q5gYgQftxZmGif6Z7NXZqmBr8km0+SxYtdIwks5OkF8/F1m1E9\nMBHOKMWlAllET10iSTYAzGbjsNDyrnCDZVl9MczTbekGBDUf7rWAXVg8cmoLH/niCeSb1/sgMszt\nShY/UTBxUhBp7QWz7bbN08xyc/hVfM0wR3pctlsFM4ckuyv669mlbQgCcHh68ArmYTLD7OP8ySai\nkKNiR8EJEPaRt2DuNTzaqqhcjR+Hwe9imBs+GOaJTAwbFdVhL0nBPHeFFMyiKCApR3pMv3jvW/Y1\n1CsvrjZ0roYuWTe2j9ORsYqkD4YZsNctFVXHxWKd2wfhcuGwD/PZdDyKWqPT16Su8eVUZyhjETVN\n536OXEkYrMn2AUClYSIhiaFKjsdSErXAMC0LW1VtoBnmw1NpvOHacbwyxDzSbiQkEfGo2NNN7gZp\nPIRdGO2fSOIrx7dgWVZP57ms6NgxHL5jbtj4mXvmcd1MGneFHCfVjhtnh2ABOJ+vU49Bqa6H5pAN\ntEuy6c2UtbKK0bQcykzupQKZmXv9teOXbBvDnE7m/RRjbtg1msQ3zxSgm5brvfPEWhURAdg3PtgM\nM2nKDF9CNQlRgrBGDFTdhGkBiYAu2Qk5glxC6pBk80aJEJBF8HfPFXDH7hEAwFhq8Bb6ZNF8eDrD\nnR8LtBmbaQZysD+DZPzygOmSzbGPY20z4gQkso9Lkt3l7XCxUMdkJhaa/0qYeOtLZpBLSr5UWoIg\nYCLdKWk2TAvVhsGtAMzEoz2L/a1qg6vxQzP9qjUMfoY5E4OimXa2dlzCwlYVIykpNFPZQUBSjnTc\ny/yMfcgukmzdMKFoJh/DnCTFblvB7NMlO5tofcZSsQ7LAq6dGbym09++9zYcXdnGPfvGfDUG28ko\nsmarNnRk496fkXLGIrpMw0JeP7xQcJVh7oJ9Mw73gTOalrFRcV/Aluo6dNMK3XU6TAiCgP/ypgO4\n75rRS7qNvWNJnKLIognytaajeMiSmf3jKZQUHesuBXupPnj5vm4QBQH37B2BeAmNyVLOIoLuwlxS\n9NAcsgG+InC9GVk0yLhzdw4/d+9O/Ppr9l6ybWQTUYgCn+lXmJFWu0YT0E0LR1fKrn8/sVbFrtEk\n90LzcsEvOxEEZL93y+TaUWsyj/24mE9l450Fs8/P/LXXXgPAZrs3K01TrQG8xshi7r4D/hpRZD+0\nzxHXVP7rIuZi2uVk0HLs41xCQlnVobcVDGXV/i0ZjueNFBEhRQRHDjuIZkUEO0eT+Mk7d/p+33im\nU9Jc82HKBjQZ5u6CmTMeTY4IiIhCT8G8rWgdfhQskJlt4rFxfquGnQNoytYPUnK01/SLt2CO9I41\nOFnoHJ+RczEiJd+F9zu0F8xHmmMN1w5gfOhNO3J41+07sHM06UuiTdQUW5W260jlO0YRUUAqFuk1\nDesjweGFjL4KZkEQ3ikIwhFBEExBEG4N60tdTlQaJtfDyg/GUzK2KMypMxs2gAuR5xvXTKZwcq3a\nYcLSjXxNgxQRQpsxd7bdlIqe7pJlm5aFsvrCKJifD9CyKduxXQ83lmEsJSMqCo4TrhvWy6pjSjao\nkCIi3nv3PPdiLwhEQUA2IaHg4VjdjwOzG+7ancNYSsKHPn/MdSbtQqHuOLkPMn7y9jnEJRG3zGcv\n2TZIQ5Y1k08W6f0co5lsvGOGucUw833mg3fvwvt+aDcWC3Wc36ohKUd8MRvPF955yxx+9NZZ/PQ9\nu3y9L9ElaQb4M36BpmFRHwXzWCYGy+psBBLTUd6mfUpuFYSlkO+7g4CJroLZ7/7JuEmyOd3eBUFA\nQor0POu267rjQu8FMh5FWPKlooLZ3ODfB/2gW5Jda/B7L8RcJNnODDLHMc5SGOaEjwQIwlLnqw0c\nWSljPC1j8hL5wFwOkCi3dvVmrWEg6eMa6m7u8sYTXmnol2F+DsDbADwSwncZCFRUI3S5zGhawiZF\nkv1CMCt6vrB/PIVCXccmY947X9UwmpJDNx8jMVUnuxjuimrAtMDdUb7S0e3M6oaSomOIc0HBg4go\nYCIjY6XkXjDrpoULBQU7R66shUhQDCclDpfscBnmiUwMv3T/bmxVNddZ5s1qY6Dnywlu2ZHF4798\n1yUtDEmzr5v5agdZpPdjrDIxFOtYJNV9zjADNjOomxa+fSaPnSPJgYzVm87G8VtvvtZ3IyrelLsr\nXTPM/uYvuwpmH00J8sxvlxyT4o73t8wNJ3Cheb0Va4Pn7tsvJobinQWzDxdx8rr2xb5umCjUNO57\nUVKO9McwpwnDrMI0LaxuX3kFcyoW6TX96kOSTe6LvhjmrhlmP/eCiUzrGJ1er+CaycFjl/sBOQc3\nO5Qa/M//TLw3lqruoylyJaGvgtmyrGOWZZ0I68sMAi6FJHssJaOuma5FxiC7jz7fODDZLFrX6LLs\nfFULXY4N2IZMU0MxnFzv3DYxIbvKMNsgM5Xds3cElmXZM8whNximhmJUhvliQYFuWtgzemVJ3YJi\nJBnlkmSHPYO0n6g0uppODd1EWTGu3uOacOJyuBjm4I9oO2e4M+6F/DsvdjWvqbObVef/XylwY5gr\nPhbbbjPMtWa+ucjBbhHH6M1KO4OqQ47yJwzsGUvh3KZ9vZUGMA6nX0ykZZQV3bke1pv7ileRl+6a\nYSZs/iinW3dCinSs2yzLwrbCN/8J2GMRUVHAh//5GB782FPQDMuJfLtSYEuyO+8zfiTZtnt/e44y\nf9MoLkUQi4odDDNvhjPBcFKCFBGwtq3i7EZ14A2//GLMxW2+5uMYZeJSj0rDz5z6lYSrM8xdqKhm\n6HJf0s38g28s9ASOb1SvSrIJdjUZwsUi3W03X9MweonmvfePJ3Gqu2BuxhVcZZhtOJJsygxzWTGg\nm1boDN3UUAyrFJf2s03n0UGPLHq+4MUwm306MNOwaySBqCjg9EbnWAMxPLxU1+0LDeT5wpJkk5iU\nTB/N27gkduQMO+ynj+O+r83V/EormMn5X2hTf/mZYZajInTT6nim133M9pFnfvtCdqva8GU4t2c8\nhaWignrDQEnRr7yCuelWTwrl9WbTdJLTxT4Ti6Kitq4BMhrHyzAn5EiPZN8wLWQ41wPpWBS//oaD\nqKg6vn3GjmO8VAkJlwtJOeJI5YGmXJc3uk7ubVo5M8w+c5Rb7+fLcCYQBAGTmRh+sFRCtWFgzxVW\nMKdjEcQl0WnMaYaJhm5yHyOaJPvFaPrledULgvAVAFMuf/oPlmX9I89GVlZWYBjszMlBgGVZ2Kxq\nyMoWisViaJ8bh32T/uSTK3jpTAw3z6ad7T1+dgtDsQgatTIa7qlGLxqIlu2we35jG8Wi++Jso6xg\nRzYa6vEhmM1E8Oi5OgqFgiM9XN6y43AETbkk2/QDwzAu+3ewLAsCgEK56vpdLhbtm3LMaoT6XYdl\nC2vbDWzlCz2zSUcv5gEAI9Fwt/lCRUK0kK/S9wVZkIhGOPur/bycz8o4tlzq+NyFpi9AHFePD4Ec\nEbC57X4NAcDSZgkAEDXqKBZ7m1OGYWB1dZW5DV2pwbSAi8srkCIiVjYLAIBKMY9VsM0V2zE7JGNp\nu4FsRPPc5iBB13Xm9x0yLaRjETz0g4u4dcK+p2zXGxBNmet3Nur2PlxcWnFcsfOlKmIRcL3faErB\nz61sYXXVXnwurJcwnopw7+eRqL22+NaRBTR0ExFDeUEdIy9Yqr2Pzy6uItZI4szyJgDArBaxqrkb\nDHa8X6vDMC0sXFxBQhJx+qL9HlGtYHWV7pVCEIWBYqXu7NO1pnkXGjXu/Xz/DgkH3nUAP/4JW4wp\n61XoevSKOU6i2UBFaWB1ddWJwzMbfOehoNnPhlPnlzHZnLW9uGbf+5RyCaur7NQUAEhKwFqh7Gyv\nUK5DAt81SJCLi3j8nH1/HBbVK+bYEAwnoriwXsLq6qqjbDLU3nPY7Z4ZhYZCpXU8TctCXTOhq3W+\nYzyAYzxumJub83yNZ8FsWdYr+/0i09PT/X7E84KtigrVsLBrPItcLhfa596ZSOPVhyr412ObeOyi\nivuutQ/Md84W8PiFCn7xvl2hbu+FjMkhGQVVcN0flmWhqBiYGk5dkv01O1qFbm4imsg4xm/Git25\nnB3PIZe7vJ3HYrE4EOdJUo7AFCXX77JQsV0m58ZyoX7XXRN16OYmDCmJ0Uwnu7BaXcdERsbsxKVz\ncX8hYWq4hG0lj8xQ1tX4RG0y9WO5dCjHqP28vGYqg2eXyx2fq27YD+gdE8PI5a6s+bCgyMSj0IUo\ndf8rsBt1O6dGXUeESqUSpqbc+tgtjI+oANaQGx1HJi4htmjfy3bMTjlzezz4t3er+C9fPIEb905j\naurSRdaFjdXVVc999KpDm/jq8Q2MjE1AjopQ9KMYy2U83wfg/7D35nGSXNWd7+9mRO5ZVVlLd1fv\n3VpBAoFYBdgYBM8GG7zy8TJjNpsxzNge8NjY5vl58PN4PPYwA9jmDdjGGGwWgwUYDDa7kNkkISSk\n1tKtbvVe+5ZVuWcs9/1x40ZGZsWNyqiKrIyqOt/PRx91ZWVl3Yrlxll+5xyMjzYBzKI4vs9tHMS1\nWRQyqZ5+HgDy6cfQZO33z9fO4ulHRnr++WcgD3zxMi5VhcN9ZP9Yzz+7EzjaSAG4iHRhBJOT46ih\nhEJaxzXHDvf08wcnDACzyBfHsX8oDT4nnORrj05isodM4kh+CtWW6R7TEoTDfWT/eKjjLN4qHOan\nXXcEa8uLu+Y8jY+U0LhQxuTkJBqGBQ5g3+hwT3/fsSUGYAqpwigmJ8WzITUrAknHDx3AZA+qlpH8\nJZhMd3+fgQuYGEqHOr5Hxufw8Kxw3p9149FQ++NOYHLkEipWApOTk+BOI8jJidF1x8hvz9xXXMYD\n0zX3dVmvvm90433KsixkMrtHUUGSbA/TJXEhHRyJ9mYppHW84yefhNtvGMfXzy65r8sRSj/9tN2x\ncUbB5JC6VrXStGBYvG/Ng0YdmZV3RIGURlINc5tscv38UImUAkctyZZzsC8srZfrz1eaONijRG8v\nMJpLgkM0X/Ojn30TnnJoCNOrzQ6Z6bLTxI/KTtrkU9q6ujAvKzUDKY1tqTGbrIOV46RqIbtkS173\nvGP45199Hp51fOc4y73ygmvHsdYwcWm5Bs55qHFr8vh6x+KEqd8ExFzrRSdradkcs6vhmkKdmMhD\nSzDcd0lkx3abJFsGi2Qd8vxao2c5tvfnZdMi+Tm9TkIp5pJY8TQhXXNGmG3mOL/phScxOZxGLkR9\n7U4g78xhltllAD13YPafoyxrmHu7j7pHh9WaFgohj7G8piYKqV3ZgHeikHabC4adwDCU7mz6FcUE\nh53KVsdK/RRj7CqA5wH4PGPsi9EsazBMObWz/TK+bzk8hLlyy734SnUTeiL6EUk7mQPDacwpalWX\nnAdXP5p+Ae2Oiyu19uZQpqZf68ilNGUNc9thjvZ43TQpyhge9pnz269GcDsVea2qHDI5E/RAH6Lo\nTz88DAB4cGrNfU1OCKBz1EbWVqpYrhkY2+I0gO45w9UQ3We9MMbwpMndqQyQ83jX6gbqhqhP7bXp\np1/TMNH0q/fju28ohQVZn1tuwrTDNYVK6wlcM5HD3RdEWcpu67WxzmEuN0PtW9Ixlj9fC2nsi2aT\nDbcGes0NoIffy37z/7oe33jrD4X+ubgzlNVh2RwrNSN0UM5vjrLcF3s9R/mU5u5tgBwNF26P0x0l\n1kufvH/HSIjDcKiYwVSpDtOyQ98DQxkdhsXRdPa52iamLewWttol+9Oc8yOc8zTn/ADn/EeiWtgg\nkA7zZJ8c5qOj4kF4brGG2//8Hnzq+7MYzSV35Q26WQ4OpzG31lzXHA0Almv9bR4ko53ezXuu3EI+\npYUygnY72ZSmHCvVrwzzcFbHsdEMHpmp4HMPz+M7Tr2R/J1xnA87KIbcLsz+DrMMSO3vw9zqJ0/m\nkdIYHrzaDmwsVgwMZ3Skeuz8uxfIpzVUAkaziWt6a86PrKttO8wi+9lLB+e9wrBjsK81zPaIxx7v\nC7nneDOQ9ZDdY73ZsWnH/gjbFOrGA0NuA7ndm2F2umSXm6Hkst0ZZulY9ToSZ3Ikg6ZpuzaBzDAP\n77LAxFa47eQYAOCrpxfcTHGxx+vQb45yqW4gpSd6drq7O6GHGQ0nufmQCPT+3DM3rmPdidxyeAR1\nw8bZ+SqqjqQ6jMMMtO0JN8O8B21ismA8TJfqSOsMxT5thscdWeldZ5exVDVQ6sP4nZ3O5HAaFofv\n3Go5EqJfmSrZnfTX//FR/NfPPw4AeHS2gidP7q6uiVsll9SUkuzlmoFsMtGXAMPNB4fw8HQZf/71\ni/jg3VMARAOKlZqBMerA7DKUEce+3PA/R/PlFvQE68sxS2oJXL8/3zGebbHawgSdnw66ZYTdCNXE\n1gIaaTfDLNQg1ZYZ6ezt3YBUY6zVDbcD8/6h3hzWUef8yEAuEH4+qXfO75RTEnYo5Jxeb/a/V0dl\np+A6zA0Tts0xX25i3yYcZnmvScl9r0GjyWFxLcyuimtjdQsZ5t3KUw4N4+hoFp8/NYtlqSbqcb/3\nm6M8u9rA5HC650RSPqWj6txDtu2UVYScLvCKp07i3re9CE9xFFK7jacdHQEAfP9qCbNODfNEj6PV\nhrqCTrVNTFvYLZDD7GG6VMeBQv8yvjJy/I0nlt3XdtsDbqvI7P7M6npZtqyFHOtTDXPRk9H5zEPz\nMCwbp+cquPng7pQjbpZsMhGYYe5XtvdJB/KYK7cwV25hxjFu1+omLE5yXy+9ZJj3FVJI9Gmfm8in\nsFIzcGq6jPfcdQlXVxq7ronKVimktcCxUiv1rWeYs8nOmemVZnhDcrfjOswNE/MhlRfyOeQd4VZr\nhcswZ1OaK3FcqYUbeSR5qmPknxzP7br7TM6krjRNzJWbMCyOw6O9BxTc7Jhr7IfLPrr2yFrD+Ryj\n43MJUbLxIzcfwL0Xl3F5WTTOGu+xDljOUfaq+mbXGm6gohdk8JFz7u51YSXZjDE3ALYbOTaaxWgu\nie9fWcXpuQqSGsPJHscEFrruIXmM92INM931Hn7i6YfwlPH+xRByKQ37CqmOOaVR13rudI44QYWr\npQaefqQz2icd5n45ZN3Zl7PzNRgWx80HC335fTuVXErDbNl/3EM/HeZjY21DaXatCc65R3Wwex92\nYek2EruZL7fcER79YDyfxDeeWMYvfuhB97WfvOVA337fTmQsl8RitYWrpYa753mJIsOc0cV+1jSd\nDHMz3HzSvcBQpi0JNZ0yoN4zzOJnlz1qqLBNv3LJdoa5PX82nE3wvGvG8PH/8Bw89fAwdG335UAK\naR2PzZbxhUfmAADHx3qfB96dYQ4bNJockRlm4TCXagaGMrrv9IG9zItumMD7v3kRnzslxgyF2bu6\n5yjPrjVxq5MR7YVCRgPn4v6puvW5ZFd7YYzhxgMFXFisYr7cxPX7Cz3vFd2S7M02j9wN7L7ddQu8\n7CkH8WNP7m8n0ONjndFRyjB3ctjjMHezXDNQzOpug4ao6VYWPO50MX/yAXKYvXhlhN2U6qYrbY+a\nY6NtQ7Zp2liuGW2HmSS/LkOOU6TOMLf6mokayyfR3YKgnw76TuTnnnkQKS2Bd33twrrv1VoWGqa9\n5cBTprvpV8tCngzJDkStZAJlJ8Oc0hM9l0nlUhpSemJ9hjmEIZlNtctbai0LeoIhpYV7vjHG8Ixj\nRSR3obMMCKf3m+eW8Mf/KsYyHRvrPcMsG6q6kuyQQaN9hTS0BMOsk2Ferm09kLUbecaxIgppHfdd\nKiGpsVAZ+GIu6UqyOeeYC5lhlntatWW5NeqFPZj93IiJQhqLlRbOzJZxY4gmjkNp8RxyM8wkySa2\ni3/3rM6Z1OQwd5LWE9g/lHLrubzMV5p9Gynlx1SfxoztdLIBNczlhtm3juLdmbjZtaZrrJIku00u\npSHB1DXMi5VWX0dn+J2LfjVS3KkcGsngOcdHcHll/Zg0OVZvq0GGTLJzrBRlmP0Zziax2hA1zPsL\nvXcmZ4xhLJd0pdSmZcOweDhJdlKDYXEYlo1qS2SnqQloJ4VM5/E8NNK7M6VrIiAiJwaEDRppCYaJ\nQgpzzj25Um1RcNaHpJZws8Jhu/uPeDLMyzUDhsXdzH4veOvcN6vS2AvsG0rjykodC5UWbgyRBOpW\nrO1lSTY5zNvMS26cwFtefAJPcWS+esho8l7gaDGDS8t1NzMiubzc6Mgy9pszc1WM55O7NnK/WbIp\ndQ1zuWn2rb4rk9Q6HK+Z1aanrp2MGAljDENpHWs+kuyGIWRr/eo0D/jL8foxwmqnM5TRsdYV1Hjw\n6ho+9/A8gPDdkruRGeamp0s2ZZjXM5zRsVY3sVBpYn+IzBYgGn9JSbYMTISSZDvvrbes0PW1e4Xu\nMV9hZefeBnubOcYjmaTrLMhxb8R6bnCcsJGQDdFGsu0Ms5S+hwmwyiBgtWW655kCg+vxJptO9Fi/\nDKhHs5Ekm9gWXn/bEfzgtaIVf8v0n2e7lxnLJ/HgVBlv+OjD7ms257haaoSSY22G/3L7CTf79shM\nmQx9H3JJDS2LuzV/Es55XzPMgBjNJpsZTa81MbPWRILtvnEqW2Uoo/tKst2xX/10mH0+e/8wGZnd\nDGf0dXXmr/n7h/A337kKADi0RWVLpqvpV7VlUubFh5FsEuWGgbm1RmjlxZjT4A5oH+ewkmxAzDat\nNS2qvfTBZ8JkKIY891l1E43vChndlfquVFu7ujnUVrjxgJD5qkqBVOwfSrsN96T0/UDIpl+AcOio\nhlmNd1ze0RCN89qj2eRoNRmU2HvHmBzmAXHjATGqqNdOdXuJpzldP09Nt2e5zq010TRtHAtxo2+G\n1z73CN7xU08CACxWDRwgQ38dsrZyqWv0V61lweLtsUb94OefeRC//kPHMZZL4ux8FfdcLOGWw8N9\nq2vfqQxlNN+mX/0ezQZ0OsxPOyyMKAo8rWcoLcahdAeeJBNblM3Lpl8NjyS7QJmXdQxldKzUDFxZ\nqeNYiIZSgNgL3QxzK7xUUY6gEhlmizJjPqw6e9bPPvMwPv2m20L/vDfDvJl7oJDW3C7MyzWSZKuQ\nGeZuu2AjDo5kUKobqDZNN/gUpvSuLclu1zDT+Lz1eMdIHQ4xuk5LMORTbXtiuiQCi2l977mPey9E\nEBNefMM4Pva6p9OMXx/+3bMO4dHZCr5+tj1+6/KKiDweG+u/JNs7M5YM/fXIoMWFxXpHzV/ZGZMz\n1MfI40tvnAAAfPfyKr56ZgnVloVfe+Hxvv2+ncpQWkfFZ2zRdtR8ez/7z151E05Nl/uqOtipyMBS\ntWliJJsE552O81bHfkmDpmFYsGyOumGTJNuH4UwSd84tAgBOToR7Ho96MsybkSq6GeaWhSpJsn1Z\ndTJbP//sI5uak1vI6B1y0rDZx0Jax9WVOipNC4bFSZKt4Lp94t45ETLodMgpPZlebbi1zMUQzyfp\nMFdbpjsRYC9mPzfCG4TYjMpCKgemS/VQo912E3svRBAjbjpYoAYfPmgJhqPFDGotC7ZjRD48I7LN\nx7fhRvVuLNSsaD3HnaDFG//hYfzKx9qyeRmB3I4ZlU87POzKr154XX872+9E/CTZp6bL+Nu7pwD0\ndwyXbGSY0hhGc0m88Lqxvv2unYw7L9upYy4HzGXeDIkEQ1pPoGHa7cwLZTDXMewp57hmIpyxP5YT\n9a2GZWNtEzN6pcNcNyzHmaPz041s2HUoRFbMi+znYNvcafoV7hgPZUTwcdlp7tavsYk7nXRSwwdf\n90x84LXPCPVzMts5XaqjVDOgOxnNXpF72ls/+TBOTa2J1+g+Wse+LSR/vOVDV0t1HB7Zmw4zhWGI\nWCI3wVrLwoWlOv7865dw02Show6jX+RSGl5wzSi+dX6FHo4+HPAEEe69tOr+Wzpo/cwwS6TUR7Ha\noQAAIABJREFU9+lHhkN1fNwr+DX9+uDdV3HfZXG++jn/XUsw/MGPXueWVhD+dM+3XKy0pYwvv2lf\nJL8jk0ygabTnk1LmZT3esqjNZJgBody46nQ8D5N9WSfJJgXAOv761c/Apx6Y3rQqZjgrgho1Y3P3\ngJR0S+k9ZZjVvODa8dA/c3CknWEu1Q2MZJOhEknepnAfv0/0f6DA03q2oio7OZHHfZdWUG2amFlt\n4GU3H4hwZTsH2p2JWOLO1mtaOOvMQ37HTz1pyzLFXnnHT96ID9x9FS++nrJj3ajOwZqbYe7/w+rW\nI8P4o1fcgNtvoPPjx75CCkuVFgzLdru8n1uoud/vt0HxU0+b7Ovn7wakTF3eNwuOw/x/fvZm3Hay\nGMnvyCQ11A273T2WDMl1vPKWg/jDz58GEN4ZkkboSrWFqVIdjAEHQzQs8kqyqYbZn2efGMWzT2xe\nRTSc0bFaN1zJfNi9L5/WUWtZWCw7DjM1/YqU/UNp6AmG6VIDqzUjdAPPtJ7ALz73KD58zxUAwDOP\nFUN3Ut8L6FoCtxwZxiueenDjN3fxuucdx5cencdffeMiDIuHqoHeTZDDTMSS9qgAC6t1Yext56zd\nfFrHr//QiW37fTuNpMZgWBxDaQ2Xlut466dP4wcdafR21KsyxvDKp+7v++/ZqRwZzcDiYqbv0dEs\nrqzUccUz85dKQQZP93xLmWE+VExDi6iJXUbXxCix5t7tbLoRxVwSr77tmDt+Kwxj3gxzqYEDQ2mk\nQjTDyXkk2aKGmc5P1Axnkmiatpsh3kyGGQC+fX4JALCfyrQiRUswTA6nMVWqY7VuhKpfBsSz7O2v\neDLG8yn82deewGufd6xPK935fPKN4ZvmAcCzjhdxcjyHj94rghJbHXm4U6HdmYglMhNSaVoo1Q0k\nNeaOEyIGzyd+6Vb8/ucex6OzFXz30irOzFdxZl4oAbZDkk0Ec8R5oP3pl8/j6UeG8Rd3XRrwiohu\nhpyg4G9++jQ+9OpbsOgY9BMRSj6zyQQahuU209mO/gI7kf/6Y0/a1M/Jkp3lagtTK3UcCdljQzYI\nq7aohrlfjGTFNf/AlRKA8H1JpMP84Xuu4GU3H8BkyFndxMYcHcvh8nIdhmVv+vj+hx88iZsPDeNF\nN0xEvDqCMYbnnBzFx+8TPVBunBwa8IoGA3kgRCwpeGqY1+omiiHrWoj+cs1EDi+7aR9svn6MRIGM\n8oEjHeZvPLHS4SwfLWbw45SZjwVe5/VPvvwEFisG0noi0tFP6aSG1YaJx5ymidfto3r/KGlnmIUk\nO6xUUTrIf/DPj4Fzqr3sB7Kp2yfum0Ixm8QzjoUrd/Dej6+5jbKX/eDEeA4XFqso1QyMbFJJmNYT\nePGN+8hO7BPPOi4UhMVscs8GjciyJWKJNBwqLROlukljaWJI0YncX1qud7xOM5EHzz6fGb4vv2kf\n/uiVN9D5iQle52g4o2NmrYHJ4XSkBt/zrx3De++6gPm1Jk6M50LLHYlgZL3lfLmF2bVmaIe5ewQV\n1ZhHjzxHD0+v4VXPOOz2dOgVbwA4bBd1ojdOTuSx1jCx1jDdKQtEvHjuSdEv5i0vvW7AKxkc5IUQ\nsaTg1HLVmkJOKJ0zIj4MZ8SD7eJyHcOZ9V2ZicHRXQP740/dj//2ihsGtBrCD2/zvIVyC5WGhUMj\n0dZH/scXXoN/fmgWl5ZreOUt1IgtapJaAsMZHfdeXIZlc1y3P1yX7e7mRFTDHD1eJcetR0dC/7y3\nCzN1yO4PJzyd6sM2/SK2h4MjGXz//7l9T/fBIEk2EUtk069Ky0KpbtImGkNkEOPych3FrI7PvemZ\n+OCrbxnwqgjJf37Rcfzisw8hrSfwnOPhDUWi//z2S6/BzQcLuLzSwOVSHYdGopW6ZVMa/vCVTwaA\n0FJUojfG8incd0nUx95ymO6zuDGSadsOYWvMgU6HmeS+/eGkJ3NPyZH4spedZYAyzERMyXvGbQiZ\nDl2qcUMGMcpNC9dM5HB0NIujmzBIiP7wy887Kv7//KMYpfsnlvz7Zx9CPq3h7Z8/i3LDwuGIM8wA\n8IPXT+DTb7oN19O88r4wmkvi4pJoLnVsLPz+90vPP46HplZx36USElQuETkjnr3v6CbOT2GPOwnb\nweFiFowBnAOH9ujIIiL+0E5AxJKUnkBSYyg3TXeYPREvvIbICDlksWU7x7ER4bnOk12JOsMsecrh\n4b58LgE85fAIHriyiiOj2U1lIN/28hvBOcedjy/iRddTh9+oGfJkmMPMyJZIh/m6feHk9kTvJLUE\nvvTmF6Bh2LiRAntETCErl4gt+ZSGpYoBw+LkkMWQ4YwOBoAD1KiDIDbJzQfbBmLUNcxE/3nby27A\nvkJqS5J3xhhuv3FfhKsiJN652N01470wlNHxv1/1VDzvmrEol0V0cWKcAhJEvKEaZiK25NMaLq+I\nDszkMMcPLcHc8UWjlMUkiE3BGMPrbzsCAJuS9BKDJakl8B9/6Bq3iyyx+/jxpx3EviEKZhHEXoa8\nECK2HBvN4jsXStATDLcc2puD0uPO868Zxcfvn+no+EsQRDje/KLjePVzDlHgiSD6wK+96JpN1S8T\nBEFItpRhZoy9gzF2mjH2EGPs04wxasNJRMYf/Oj1uG5fDm9+8QmqH4opL7peZFUySRKrEMRmYYxh\nnEbWEERfePNLrsNP33p40MsgCGIHs9UM85cBvI1zbjLG/hTA2wD8ztaXRRDA5HAan3zDMwa9DCKA\n518zivf9/M249Qg1FSIIgiAIgiB2H1tKC3HOv8Q5N50v7wZwZOtLIghiJ/G8k6PIJLVBL4MgCIIg\nCIIgIidKHeUvAfjXCD+PIAiCIAiCIAiCIAYG45wHv4GxrwCY9PnW73HOP+O85/cAPAvAT3OfD5yZ\nmeGWZUWw3P7TarWgaZQtI+KHZVl0bRKxg67L7ceyLCST1CAsCNM0oevU15SIH3RtEnGkH9flZmbT\nD4IjR45suNANHeYNP4Cx1wF4I4CXcM5rirdt7ZdsIxcvXsTo6Oigl0EQ6yiVSigWqa8eES/outx+\nVldXcfDgwUEvI9bMzs5ictIv1k8Qg4WuTSKORH1dWpaFTCYT2ef1mQ0d5i2FEhhjLwPw2wB+KMBZ\nJgiCIAiCIAiCIIgdx5YyzIyxcwDSAJacl+7mnL8pioURBEEQBEEQBEEQxCDZsiSbIAiCIAiCIAiC\nIHYjUXbJJgiCIAiCIAiCIIhdAznMBEEQBEEQBEEQBOEDOcweGGMvY4ydYYydY4z97qDXQ+wdGGNH\nGWN3MsYeZYw9whh7s/P6GGPsy4yxs87/R53XGWPsz51r9SHG2DMG+xcQuxnGmMYYe4Ax9jnn65OM\nsXuc6+/jjLGU83ra+fqc8/0Tg1w3sbthjBUZY3cwxk4zxh5jjD2P9kxi0DDGfsN5jj/MGPsYYyxD\neyYxCBhjH2CMzTPGHva8FnqPZIy91nn/WcbYawfxtwwacpgdGGMagP8PwMsB3ATgFxhjNw12VcQe\nwgTwm5zzmwDcBuBXnevvdwF8lXN+PYCvOl8D4jq93vnvVwC8d/uXTOwh3gzgMc/XfwrgXZzz6wCs\nAPhl5/VfBrDivP4u530E0S/+DMAXOOdPAvA0iGuU9kxiYDDGDgP4zwCexTl/CgANwM+D9kxiMHwQ\nwMu6Xgu1RzLGxgC8HcBzATwHwNulk72XIIe5zXMAnOOcn+ectwD8A4CfGPCaiD0C53yGc36/8+8y\nhOF3GOIa/JDztg8B+Enn3z8B4O+44G4ARcYYDWYlIocxdgTAjwF4v/M1A3A7gDuct3Rfl/J6vQPA\nS5z3E0SkMMZGALwQwN8AAOe8xTkvgfZMYvDoALKMMR1ADsAMaM8kBgDn/N8ALHe9HHaP/BEAX+ac\nL3POVwB8Geud8F0POcxtDgO44vn6qvMaQWwrjiTrVgD3ADjAOZ9xvjUL4IDzb7peie3i3QB+G4Dt\nfD0OoMQ5N52vvdeee10631913k8QUXMSwAKAv3XKBd7PGMuD9kxigHDOpwD8LwCXIRzlVQDfA+2Z\nRHwIu0fS3glymAkiVjDGCgA+CeAtnPM17/e4mAFHc+CIbYMx9goA85zz7w16LQTRhQ7gGQDeyzm/\nFUAVbWkhANozie3Hkar+BERA5xCAPPZgNo7YGdAe2TvkMLeZAnDU8/UR5zWC2BYYY0kIZ/kjnPNP\nOS/PSdmg8/9553W6Xont4AUAfpwxdhGiTOV2iLrRoiM3BDqvPfe6dL4/AmBpOxdM7BmuArjKOb/H\n+foOCAea9kxikLwUwAXO+QLn3ADwKYh9lPZMIi6E3SNp7wQ5zF6+C+B6p5NhCqJJw2cHvCZij+DU\nLP0NgMc45+/0fOuzAGRHwtcC+Izn9dc4XQ1vA7DqkdgQRCRwzt/GOT/COT8BsSd+jXP+7wHcCeBV\nztu6r0t5vb7KeT9Fr4nI4ZzPArjCGLvReeklAB4F7ZnEYLkM4DbGWM55rsvrkvZMIi6E3SO/COCH\nGWOjjoLih53X9hSM7ss2jLEfhajX0wB8gHP+3we8JGKPwBj7AQDfAHAK7VrR/xuijvkTAI4BuATg\nZznny86D+D0QUq8agNdzzu/b9oUTewbG2IsA/Bbn/BWMsWsgMs5jAB4A8Iuc8yZjLAPg7yFq8JcB\n/Dzn/Pyg1kzsbhhjT4doRpcCcB7A6yESAbRnEgODMfb/Avg5iOkXDwB4A0TNJ+2ZxLbCGPsYgBcB\nmAAwB9Ht+p8Qco9kjP0ShE0KAP+dc/632/l3xAFymAmCIAiCIAiCIAjCB5JkEwRBEARBEARBEIQP\n5DATBEEQBEEQBEEQhA/kMBMEQRAEQRAEQRCED+QwEwRBEARBEARBEIQP5DATBEEQBEEQBEEQhA/k\nMBMEQRAEQRAEQRCED+QwEwRBEARBEARBEIQP5DATBEEQBEEQBEEQhA/kMBMEQRAEQRAEQRCED+Qw\nEwRBEARBEARBEIQP5DATBEEQBEEQBEEQhA/kMBMEQRAEQRAEQRCED+QwEwRBEARBEARBEIQP5DAT\nBEEQBEEQBEEQhA/kMBMEQRAEQRAEQRCED+QwEwRBEARBEARBEIQP5DATBEEQBEEQBEEQhA/6Nv0e\nvk2/Z8tMTU1hfHx80MsgiHUsLS3RtUnEDrout59SqQTG2KCXEWsajQYymcygl0EQ66Brk4gjUV+X\nqVQKo6OjkX1en9nwgbpdDvOOwbZtaJo26GUQxDro2iTiCF2X20+z2USxWBz0MmINOSVEXKFrk4gj\nUV+XjUYjss+KAyTJJgiCIAiCIAiCIAgfyGEmCIIgCIIgCIIgCB/IYSYIgiAIgiAIgiAIH8hhJgiC\nIAiCIAiCIAgfyGEmYk2pZuAH/uddODW1OuilEARBDIxz8xVc//tfwrn5yqCXQhDELsK0ORYqrUEv\ng9iA711exf1XyBYeFOQwE7HmO+eXMFdu4r13XdjyZ739nx/Fh75zKYJVEV4452TE7xHqLQuWvWOm\nBO4qPvvgDADgi4/OD3glBEHsJj57ag6vfN99qLWsQS+FCOCXPnIKr//wqUEvY89CDjMRa6RtriW2\nPnP0o/dexR/9y5ktfw7RyUe/exUv/4tv43uXVgLfxzk5WjudH33Pt/GBb18c9DJ2HVdWavjjfz0T\nGIxomDYAIK3TY9vLE4s1vPbvHsRa3Rz0UggfDMvGFx5doP0/xlxYrKNu2JgvU5aZIFTQk5eINdKA\njMJhJvrDnacXAABLVfXD9sJiFTf81y/jm+eWtmtZRAhqLRM/8mffxG984iGl02ZaNq6u1HFpqb7N\nq9v9vO3Tj+Bvv30psPSkYYjsTybZfmyfnqvgaf/jmzg1Xe77GuPK3RdK+P5UGQ9OrQ16KYQPH71v\nGr/zmTP4wqOLg14KoWDRkWMv18hhjis7ISB4/5VVPDa7e9WG5DATsaZliayKxshhjitz5SYAQNfU\n28nf3X0ZAHDPheVtWRPRydxaA3/vnAM/ZlabOL9Yw+dOzeKhq/5OW6UpHLZKI/4P7p1KkCSy6WSY\nvfGMey+Jc/W5h/euTHt6tQEAOLtQG/BKCD8ahrhuf/ezZ/CGj8ZPTrpWN/Hx783AcGyNvciCE+xe\nqhoDXok/hmXv+RrrKWefizNv//xZvOeu3Vv2SA5zRPzOpx7Gaz9436CXseNYrDQDv192jPMAX6wn\nzD38MOw3c2tiI68HGPtfc7LQI9nktqyJ6OQ1f/s9/OHnTyvvt3KjbSjVDf/zWG6K91Sa5DBHTSap\nAQBW62qDtek4Ht77bNS5n5ZjauhuB1Mlx2Gerw54JYQfI1nd/fd3L8WvYdFXHl/EH3/pCfz6Pz46\n6KUMjAVHin211HCVLHHir751BS/9i3vde30vcjXmf3vdsHBlpYGV2u59FpHD3AOWzTdsdPOpB6bx\n7ScoexaG715cwQv+5124vKzODMhsFttihlnlBBBbR26QQdmxGceprpKzNRDOLwpnQu0Mt89LSxFc\nksErcpijJ+s4zEFlDZWWOO7VVvv4W05daNDP7XakIXl2IX4Os2HZ+ODdV2PphGwXMsMsiVstc8tR\nbnznQgmXlvdmucmis3+8+86L+On33z/g1aznnKMeed831Sqp3Y43WCCv2ThxcakODmB1FyvQtuww\nM8aOMsbuZIw9yhh7hDH25igWFiee+yd34vUf+l5P7w3KshGdXF2pw+bAVEn9kJKGfHOLG0TVc17i\n9sDeyXidJ5UzZlo25CGvbnB//OkXH8cDl0uRrY/ovN5V+5NXZq16GEuHubyLH4iDIuVIaBYDZIcr\nTn2hNzAl/728i6P6QXDOMbUqVBMXluqxk9V+6bFFvOvOi/jLb10Z9FIGRncgdaNnwHZT9zj0//ro\nwgBXMhhqLcsttwGAqVKw6m8QyL4NXzq9GEtncTvwOsxxu4cA4JwTsAxSSe10osgwmwB+k3N+E4Db\nAPwqY+ymCD43NqzWTXzn/HJPjtbVlb0ZodwM0hkuBRh7aw2ZvQw20k/PlgMzX3UfI5PYOkseA1/l\njHkzlkHnaLVu4P3fvIjX9RicInpjenXjB225B4e5QhnmviGDTUEOs5Rde/cv+XP9rD28sFTDD7/n\nXrdXQZxYqZuotSw89dAQTJvjYswa0iUcZdRezVwCQK0rkLoas+ZFtZYFBuDpR4bxb+f2nkowaM+J\nC7LhVcOw8cDVvdncb87TwTyWDvOiUAGUmxbMXTp6cssOM+d8hnN+v/PvMoDHABze6ufGBdtz4k8r\nur953xMkLyY6kUZ60ANUvifIyTUsG6/6y3vwd99Ry3W8P18mg79nbJsHyqi9zrDqHHnVAUGfJe+d\nobSufA8RnvMeqaoqqOG9JwzL/2EnHWVymKNHyqyXAno6+GWY6y1xb601zL6VnXz4u9OYK7fwtTPx\n63A/7zjxP3DtKID4ybJlxvvKSh0XlvambVBr2RjO6Pjtl54EED+HuW5YyKY0HC1mdnX9pYrFrnKO\nOA4kWW2YeNrhIegJhm9fCB5fuVuZ9zwb4pj0ecLTdHG3qtAirWFmjJ0AcCuAe6L83EHirRe756J/\n9NFrQF4OyDB/4ZE5vOUTD0W3uB2OPG5BEo5KDw7zSs1A07QDpd3e87hbb+Z+8M6vnMPT/+hrSkfX\nm41UGeytDodZfR4vO1mYfUPpzSyVULDmud6VDrM3w6yqYfY4zFTWEC1yf+s2XiVN03bvHa/axnvP\nza31JwNsOgEUXYufJS2vW2lMPz4fL6dU3nuPz9fwk391/4a9UDZLw7DwYEwzb/WWhWJWx5MnCwCA\n1Ua8nNJay0Y2mcBwRu/YK6PkaqmBd3zlfOxKBgCs+5v3x/D5u1o3cHA4jWsmsrFTkWwX8+UWDo2I\ncxPHDPMTizUknWfEbpVlR5bKYYwVAHwSwFs45x0798zMDCwrfifYD9M0MTs76349s9Y2YKYXVjA7\nu34zkTVUAHD66qLvewDg1/9BOMu/9YJ9sTQ+tpv5ZXGZTC2WOo65l6Wy2BzXak3le84tivdcXVpT\nvmd6rn1JXpqew5C982bFdV+b28E/fFdk7S9cncFEfn2H61mPgbpUKvuub9pzf6xUasq/4ZFLYjRO\nIcm3/e/czUzNtzODMwtLmB1db7TNLbe71y4ulzA7u/7RML0gasttDly4Mo1cSjSqGsR1udtYrQrZ\n/Nxq3fdYemuUS5U6LMtCqVTCSqVtPJ6dXkZRi15eWW2ItdVqdZRK8eovMLMk9nXdauBYMYXHpkvu\nGuUxGiQLq53PmUcvz+NoMXqH5DOPLOPd35jGp1/7JBSz8VLorNYaSGlAwhDX6szSGkqj8bF/1qp1\nZDSGFDNRaVpYXF6BHnGa9T13XsW/ni7hmpEEXnJ9MRbXpmRhRcxw/x8vP4b3fHsW1aYZm7VJVusG\nMgkb6QSwWm3Gbn39xrBsLFUNPPdYAdOrTcwtraJUiD74stnrsmZYmF5t4qmTOZyareHqQglFrQXO\nOa5evRr5OvvBkSNHNnxPJDsrYywJ4Sx/hHP+qe7vHzx4MIpfsy1cuXIFk5OT7tdLdtvRSmZyHd+T\nzJltY9NkKd/3CITDrBVGMTmSiWbBOxgrIRwkM6E+Zg3rHACgZTPle56oCodgraV+T3qx/e9UfgST\nkxO+77vv0grKDRMvvnFfT3/DdjI7OxtwbfUHG2LUxtDoOCbHcuu+f7nhUV3oad/1VRJto7HFNeXf\nsGyIk5RO+38OsTnYubZTlcoN+R5bS1vESFbHat1EJl/wP/7J9l6YK45jcljsYYO4LncbTetxAEC5\nafseS9OjXDKhQdM0FItF2GwOeoLBtDnKto5isRj52hKacOBNluzL528FWxfBuEMTo7hu/yrOzFfd\nNZZKpYGv10CnKm22oeGpfVjTmlmCzYFGIoNisRD5528Fg1/BUCaFI/vHxNcsNfDz4sVk08hndOwv\nFgDMQ8sUUMxFO/7w4GgJQAmnl0z8zLOLsbg2XZJib3nWtQdw+5KJT35/Lj5rA2BzjnLTwv5iHot1\njpWaEav1bQeyD8n1B4Zxz+UKWCrTl2Ow2evyyrQIujzn5BhOzdZg62J9jUYDBw4ciHqZAyOKLtkM\nwN8AeIxz/s6tLylerHnkQ93jESSlWjuq3zQ3zqTLubV7nUoPTb/k7Negpl+y8dRCQP2fV8KyFiAX\n+YX3fxe/8uEHlN/fa8j51TWFlLplejowqyTZzmfkUtoGNcziwR3Uad62eSxlbYPkXV85hzcGXLNe\nuXV3Ax5JpWFiPJ8CENAl23PuKlTW0DOVpolLG/S2kCUjKjm8dyxRd5fs42NZMPRPki076PZLrroV\n5LU9lNFRzOqxkwKWmyaGMhr+8MeuR4IBZ+b6U2Mta2+XKvH6+wFxjeZSmjuPOW6S7HrLRjapYSTj\nrK8PNdZyhvq3L8QvMyr3k1xKQ0bX0DCsWJXcVBoWbA6MZHTk01os5cj9Zt5p+HVyXCQt4nYMnnAa\nfj3z6AiA3SvJjqKG+QUAXg3gdsbY953/fjSCz40Fa57NU+UMS4cvn9KUBo+X+Rh2Gx0E5R5qmHtp\n+iVnkC5VWh0N2LxQ06/NIbsdqpxh6bwm2MZNv8ZyyY5a8m5kw6OgmaWv/9D3cNMffGXjhe8RTs+W\n8X/uOo+vnVGPQ6k0TGSdsRzqGmYDYxs5zB6HiRp/9c7rP/Q9vPRd31R+n3Pu1ie3TNvXWJX30Ggu\nua5L9lBGx0QhhZk+OcyyrjqO8zXLDRMMQCGtYSSbxFrDhB0jY3+tYeLQcAY/ccsBnBjL4vH5/jjM\nsoO6qgZ+kNQMG7lUAkktgVxKi2fTr6SGYcdh7kdgSAYJrpYaHTZlHJCNA7NJDZlkAjZXN34cBPLY\njWR15FN73WHOAlAnMAbFuYUq0noCNx10+hTE7BqPiii6ZH+Tc84457dwzp/u/PcvUSwuDsgMc4Kp\nM8wrjsO3fygdOC84rYvDTQ6zoOwcW5XDbFo2DIsjpSdgWFxpyC87RoJpc/dcdON1FKjpV+9Ih1nl\n6MpzMpJNbtj0azSfCmz6JR+EQcGRb58XEsd+Nc/ZaXznfFvyqQo0lBsmxvIpaAmmPLZrDRNDGR1J\njSmDft6sMgWdeuf7V0TJjirI0DJtmDZH3qkJb/kYq50Os0cx0LKQSyZwcDiN2T45zNIZi6MRtNYw\nUchoSDCG4awOm6NjpuygKTdEhhkADo1kOjrdRonMMMfRYa63hEMKiCxh3K6jWstCNpVwM+BrfciA\ne53wuHVyrxkWMskEtARDxrFRVbbuIJDXy3AmKVRqe9BhPr9UAwNw7URMM8wLNZwcz2IkqyPBgFLM\n7vGoiLRL9m5E3qxBznCpZoAxYGIoHThUXTaSmO+TYbPTkI5rSeHkSsN9NCvqiVTG/rLHSFhUBCOq\nLdMdl6ByLILkwnsVmazZaMZyMZtUnh/3POaSaJq2UlItf0cv43FmVqmsAeh0YlWBp3LTxFBaRzap\nKc9RuWFiKJ1ESk+o5zA3TYw5jd9Ikh2eRYWzVHHOyahTN9nyUTLJPWs0l+rKMNvIpjRMDqcxuxa9\ns2Rz7u6v/XAktoq8tgGg6Epq47NOIckW68qmEm42L2pkU7g4ztSVkmwAfe1EvVnqho1cnzPMa3UT\nJ8ZEdjB2DnPLQs4JaGSc/9d7KC3cLqQNXnQyzLWW1RcVyRcfW8Bb7ng0VnJ0yT0XS3jypKitz+iJ\n+DnMizVcO5FDgjEU0vquDaiTw7wBa3UDWoJhLJ9SOlrygZBNqo1N07Ldi3yWMswA2pkAlYHjzaoA\n6jrmJY/DvKAwGMQ50pHSE8rAhzfzHxT42IuoHC3p/I7kkmqn2pCSbCH59csyc87d39HLjMELS/Ey\nOvrFu796Du/88lnl970PJqXD3DBRyAhjQxWMqDiGfUpLKDPMdcPC/kLaeX+8Htg7AZWySAbqRgMk\n8Q1Z1pBPwbDadfx1Qxi74/lkR+AwKkp1EzLhHTdHBwDKDct1SIez/atB3SxrDct16LNwlqxcAAAg\nAElEQVRJ9f23VaSyarEan2CBpGa0HeZswB40KESGWcOwE5jvx/Wz2jBx/f4chjIazi7Ea/SZN6CR\nScYvw7zm6VOQT4t19mMO8W//0xnceXa5Y+pNHKg2TTw0VcZtJ0Uzrnxai9Xzd61hYq7cwnX78gBE\nUGy3qjjJYd6A1YaB4YzIzqgcrZZpI6UlhLGpeI/X2KCmX6J5U7VlIqkxNAzb97g1PVJeQC1DWaq2\ncMCZHehtwOal3rKQT2mB58jbNCeo1nYvosweO02/RjIBkmyr+zyuP7Yti7vy76Aa5omC+IyLi/Ey\nOvrFV08v4FtPLCm/75X5qpQaZUdunU2pM8xN00YmKfYwVf2aYdnuOQyqYTYsG2/79COB696LLJTV\nwTygHVDylWQbnVnoumPQ1h1jt5DWUWlG36xH7omFtIaFihGr7C3QmWEekQ5PjIy1csN0M5fCYY7e\nETEsG+WGuD6WYpZhNpyyKpnBzCYTfXF2toKoYU5gyHHG+lLDXDcxnEniuok8LsTs2SUcZuEKtB3m\n+JwjGRQeyujIp8S9FHWG9cJS+5yccjo+x4XTc1WYNseznIZacXNIzzvXs5SLD2U0yjDvVeRGlw7I\nTBqW4zAHvMdraMTN6BgE1ZYJzuE2GvI7bk1PfSyg3sRXawYODAuHWZUdqzqGZdB5nPNkgOL2UB8E\n3sDChhnmAEm2t+kX4C99l+qB4YyOumErm7cVHOPY+4DbzSyUm4HR/g5Jds3/ISWciqQw2APOY1JL\nIBkgyTYs7jpsQZLs9951HnfcP4UP331Z+Z69iCrDLO8bOcrGr7mkvIdkNtVw7o+aYSObTKCQ1sCB\nyB0yOc7kSQcKWKkZeMX7vhf6MwzLdj8natY8NcJy/nBcmiqZNke1ZXVKsvvgiKx4pkzELcMsJejS\nIetX0GCz2Jyj4Uiyk1oC+ZQWucPMOcdaw8RIVsdoLhm7+s6a0ZZkZ3Xx/zhlmGU2NZ/S3D4PUdtn\nD061neRTU/FymKXzWcw5SpqYlTVIVefBEWGDD6V1N4C32yCHeQOqTdFUJJ1MKB22lmkjqSeQ1tVy\nRukk51Nqo3UvITdBmTH0q9uTUl7pMKuMjVrLwmhO7XiL95jIpjRRo6k4R97MP9Uzt5uyARvXMI9k\nddQV4yikAzbsBj7WH3/5AJTXQ0NRQyXvwZlS3ff7uwnDsrFcawUa2eWm6R4z1biWipNhzinkkJxz\nGBaHnmCBCgzDspFOCqMlKMN8x/3TAECz5tEeywaoa5jlOZH3h68k27ln8k7AyLJF47umKWqYpVQx\n6u7l0448cf+QuMY2Y6h94v5Z/PRf39+XrJU3gyv/r1JabDdyT5NGfjapwfAoaaJCOszHRjOYXm3E\nqpxIjrHLptoZ5jhJshuGDY72+vrhjNQN0dRvOKOjEMOxSLWW7f79boY5RtdQtWVCY+LakdLxoOah\nm0FmbK/bl8PDM/FymGXQKe+5RuM0mk0+c+T6hjJUw7xnaZk20rqcT6fOvKQ01lOGeXIksyeylw9c\nLuGtd5xSZgrdur0gGaLZbigFqKOeNcPyZGfUDaXcDLPic7w1gHF7qA0Cr+HQS5dsy+a+51E61TI7\n7Nf0S45JGHdqZFUOurwG9sJYo8VKC5wHy+MqTROHi6KZjJ9yhXPuNh7KpjTf8yg7juuOSkYVUBKl\nJ8FNPZaqLbchW5wM934SJIP2npMFRYbZzR4794ffHiYDSAXXYeau45FNJlBwpIpR17ZNrzaQT2n4\ntRceh+7pohuGS8t11A1b2V9iK5Sb7Rph6TDHRZIt71vphGw02m2zyJrYF10/DsPim2oq9cdffAI/\n8/77I10X0FYo7HOUZKKGOT77gryHZIZ1OBt9F2+5B4w4DvNmnl1z5SYevLoW6bokNadcDWhPcolT\nhrncsFBI62CMoeAEBqO2z+R4uluPDOP8Yi1Wjb+6g05DMcswS39GnhuRYY7P+qKEHOYNaFnCSEzr\nCd8saPs9CaR0TWkkShnO5HAmMMJqWDbOzVe2vvAB8/XHF/FPD84oxzzJDXkkEyRDlJkX3fkZ/+NW\nb3kcZpVT3bKQTwU3NfIam1FHMHci3k15oznMBcdYbfq8TzoAMgvmVyMrHbkJx7BSGVVyHbs1gulF\nOlhBBmalYWJyOA0twbBaW3+v1Q0Lls1RcLpk+xnrMuOlJxhSOguUZCe1BPJpXSnJfnSmbdTtBYf5\n9z/7KF767m8qDSyvXHZeUcPcNDr3Od9+DkZnlsHi3H2mDDmGONAPh7mJQyNpHC5m8MvPO4KmYk50\nENJRnlP8/ZvFskWjQPm3JzUhTY+LJFvuexm9nWEGepsCEIZPPziHI8UMfu4ZBwEAj8yEtx8+fv8M\nzvWhGdWZOeG833BANASKW4a5PYNYmML9yDDLAM5wVkchraPaDN/l+Q0fOYXX/P1DygkTW6Fu+DT9\nilGX7Iqj8gTQzjBH7TA3TeTTGq6ZyKHctLAUsrThk9+fxUv/4t6+qGiqXUqVkZg5zHJ9Odeh11De\npfYzOcwb0DRtpPQE0klNKVNpWUKSnQqYYSprNPcVUoEZ5nd+5Rxe/hffxpWVnV2jWaoL42hJIUOU\nG/JQVjpa6hrmYraz0Y0XOcO0GCBnBMRNnU0Jab0qC+2VYZMku3OMTE2xAbZMjqTG3HEUfsdWnhOZ\nHfO7R6QRNe7Ii/0cO9uRoAJAZZfWyHiRDnPQQ1hkj5MYyfrLtGTgx+2S7XNcpRGW0hOBASXZq2Eo\noyuzJI86DVPG86nAmfS7hX/47lVcXq7jW08s+35fyoP1BHP3xG7kcZIZUr/jL4OHMstg2cCVFVGW\ncLSYcYNRUTcrnF5t4JAjrc8kRZ20n4okCOkwq2q4N4ubHXSOCSCOYVwk2dJeaGeYpcMc3X2xWjdw\n3+VVvPKp+3G4mMZoVg/tMHudsKgza2cXqihmdex39vVsUij1+jEWaDPUu7J3Ixk9coWCzLYNpfVN\n9xq4vCIy9f0IatQ8c7LlczxOGeZK03Jth3yfJNkVR6lyclyotcL2SPnYfdNYqLTwkfumI10X0M7g\nynM0nNFRafRntNZmqDYtpPUEkprY54bSOmotK/LSkzhADvMGuJLsZEItyXa6ZKd1TRmBl05DMZfs\nGAvSzf2XSwCA2Zi1tg9LycmsLClGnchjKR3dzTb9kg+8fFpHMjBg0ZZkGwFzZqXRuhdk8187s4D/\n9aWzSiPJ+1CqBWSYk1rClWoGnUdp2Podf+mQyyZwfr/PG/XeC5LseSfYZNrq/aLSNFFIayhmk75N\nv+SxT+sJZBRySJnxFxlmdQ1zyznXQZLsJxarODCcxr6htPJe3E2cHBedQT93asb3+3IfPDKaVRp5\n7YZe6r2wYYjAre4Mk7dsjiuOEX1sNOsalFEbkjNrTUw6DRU320G37TBHm2GW17I0JAGxx6j2qu1G\nPuOkzDXrNL6KMsMq5cOHRzJgjOGaiRwuLYfr7+B1wqKWS5+Zq+L6/XkwJq7bbMwcMjf766mDj1pO\n6s0QuvdpyHncE3mxNzzUh4ZU3rFS2RiOlao0TRSc9fVrrFS5IZ6jcj+/sBTuHjruzNj+2H0zkTuy\ntZaFjJ6A5uz9wxkdHPFJGlQ91w/QbkwZ1Bh0p0IO8wbIkVEZXfOVDQPSkBTGJufwjaxI47GYU2fQ\nALiOx053CFyHWVG31i1DDHK0ijl106+aRw6S0tTZY+9YKWWGuWVhvzOeKihTc3a+gjvun1J+f6fw\n+595FH/5jQv42pkF3+9LVcR4Xq2KkOUI7donn+Ztjkoj5UQg/RwpaVRMBGSYG66BnNgTkux5z5gz\nv+Nq2xxVJ/o+nE36Ztako53URATYz/F2JdkaU2aYZWOwpCbqyFQPw5oTdAqSdu8m5F6ikgHLc3Ko\nmFGqVtZlmBVzmDN6AnpC3EMW57i8UkdaT2DfUMrNvER5X1g2R7lhud2nNzOj1eYci/1ymN3MS9uM\nEaVT8chseINVQLtONkqntC2HdLI7GT2UMzG92sBr/u5B9+uo7Y7zS3Vcvy/nfu3WccckqCFLJmT3\nf1HDHK1CwVvjmd+kpDjtHLeHpqOtYzYsGy2Lt8dK6XGUZK/PMFciVtKUm6Kb/f6hFLLJROigkwy8\nLFRakQc1al0OabtXQzyUNNWm5QY0gHYvjjjJxqOCHOYNEJJsUcNsWNxtkOOlZTmy7YAsmzuv1jE+\nVFFwuTEuxmyeYlhWNsgwywemrGH2lyFKQ1Jdn+yVq4ima/7HtdpDl+xKw8R+J5sSlKn58D2X8Xv/\n9Ehf6om2Eznm6WP3XvX9ft3jxCq7ZMuSBT1Akm3ZSOuioRTgX8PsdsnOi+Pvm2F2ZdtptEx710t+\nFzzlDH5GtgzqDLlz4v2DFQBEU0KN+V6z8jU9kVBmmOU5S2oJFAIk2Q3DFvdigLR7NyEdVNXxkE7y\ngeFMwDFzgocBGeaWaSGT1KBpMsMMXFlp4EgxgwRjrpESZYZZGvWyP4EcORPG4SvVTTcgM9cnSbY3\nw5wJeAZsN01PgA9oy36jbPrlKqxSbYcijDNxarqMlsUxkol+vq3NRY25NPABzzGISQZzncOc0dGy\neKS1qLKvQC6lte/THjPMS9UW3vDRU5gqiXvn/GK00yHcsV/OPZTuQ1Bnq1RaptunIO2obKIeWyTn\nuScYw/GxbOgM81rDxLOOjSCpMXzt8aVI17bOYc7GyyGttizk0t4Mc/TB27hADvMGCGdYcx1Zf6OU\nu3OYxdfqOk4pu1NnmMXFpqr93SnIzIpSkt01aiio9lVuEH5R6bpH7qSasWxYNgyLI5fShVOteBhU\nmibG8ykkWHCGeWqlAZuru97uFKQBq9rY2lnftNKQMhx1hbw/fBsWOSoNWePi2yVbNv0KyDDLh/g+\n5z27vc7cW/PpZ8BJQ6wQUI4gHd2UJjL8fsEK030PQ1Lzz9B565yHAiTZdcNC2gleqTLMM6sNfOZB\nfwnzTsK2uesEq5xhGQjaX0ijbti+AdfuGcuqsVLdkuzLKw0cGxX1xdJgqUTo8Mj7Sxqrm2kI5N0j\n+ybJ9hiTKV1dOrXdyOPkSrL7kF2tepwxAG5TqV6Rgfm3vvQaANE2jWtn2D2S+T41Ptssy47DLFUU\nMoAfZR1zO8Osh+7y/IVHF/HdS6vu11H3AZCBabl/6AmGpMZicw8BQnosM8yMMYzmkpGrAGRpEyDK\nbMLWMK/VTdEccSQT+cx5bxdzoJ1hjo/DbPqubzfOYiaHeQNkBi2oGYKs4wzMMDtZaPnAUMmmZDR+\nUeFo7hRKNbH+ZcXfISXZI1l1d2XZlTyjJ5BVzMGuGWLTyKY0UUPuc37qrbZRETQru9qS82qDZW1X\nnRnAMxFvjNtJpWm60XVVNL3WssCYiL6rM8y8Q5KtCnyIphDC2A+SZI/m1V3T5Tr3ObJ5ldP2j9+7\nipe86xvKkWY7hQ0d5oZ0aHQktYTr+HppeRzdpJaAafN1x8XNMDtBP7+ARsuVdrN2p1ef49swLGST\nwc3D3vzxB/Fbd5zC/ZdLmF3bufdQtWVBlqupJOr1lgUtwdzruuYTiGuZttM4T12y0DA669hszrFQ\naeKAo4jRE+Lno5TUugGZ1OYl2dJJPlrMRP5M847VkmQC9vftptth7EfTL9fhcSS1+bSGStPquXnX\nQsVAUmM4XNxYWRWWZlcNN+AdrRWPc7RSMzCU0dxgbj+ckUrTAoP429vN+Xr7+6UjDwCHRtJYqhqR\nKtvckrZkZ1AjLj1cOBdBSZm1BITNWIq4E365YbkByxPjWcysNkMFdVadefD96LJeM6yOoGD8HOZO\nh16qKCjDvAfxjpUC1A7BRjWashZaXviqDans1CUsRhyN306ahuUaBaoa5kaX3FqVmQSAlK4hk/Sf\ng+3WsQVkmL1t74NmZVeaYvRUPq0pDQfOOaYdh3l2bedmmOXfAATMt3akQPm0pnx4tLqCRX7ZJ3l/\ntDPM6425ekt0Wsyl1DXtcg37nFnNKiflvkslXF6uRy4B3W4Wyi03m+5nZMsHUiGjK+uTO2uYWcdr\nko6xUgpHt1uSDfirMBqGjYwsj1COBhOv/9xf34vb3/kN3/fsBORezViAJNvpQCszJH4ZvIZhbVjW\n0DDFcXUzzFw4HV5DpZBS71ubobwuwxy+YdMTiyJT8/Qjwx0jtqLAr+lXOk4ZZmcd6+YwR5lh7ho5\nU0hpMG3ecyfzxUoLE/mU5/qMzsh1x2p5AhptSXY8HLKVmoFRR+UGtAP4UY4mk89R5i2d6NEh9ZYm\nPXmyAI5oy/W8PWAkQ31ofLZZ6oYNi7cnbACiUWyUnfBdp9z5HSfHs+AALvdYx2xYtlt6IJrGRXtt\nV5sWcp57KHYOc7PTYZbPC3KY9xicc3esVLsLsP9YlqRXkq3IQqd05l5YqgeGNKgWqzvX2PfOXlbW\nMLc6m34F1TCnnQx/YNOvpJDN+31OrSvDrKr1bJk2CmkdOcW8WkA8YKWhFucMc7lhBD70rpbE2k+O\n55Q1f7WWiXxKR1rXlFnodv2+Y+wrxoN5Sxb8JdntLubyZ7qRBqiUbauMOzlu5/Lyzh3NZtkci5Um\njo2Jhjm+6grPda0nmG8gwq1h1hNIyv3JUmWY1V2yvY73UIDzVzeEgxik5DhczHg+d+eqAOT9dWhE\nXZ9cN4Qx0R6H4t/JXNT4OwqMAJWGzDA3DDFOL9dhqOgRS7LbUlJgcw2BzsxXsX8oheNjWdSNaPsO\ntJt+dUqy49LboOF5fgF9yjA3u2qY3XncvRmrC5UWJgopTzOl6CXZMpEAxLPpl+zlAfQpw9wy3fMS\nNsPsdb5umiwAiHaeeVuh0FmD2g9np9aycGYu3MgzeR17HbJixBnmWsuCzdu1t2E7ZctrZSTbrwyz\n7V43APo2EWGzVFsW8p6AhryW4qIiiRJymAMwbQ7OhaQqHRBdb1ntxmDya9/39JBhlhvETm76JTtk\np/SE8u9oOjLEoIxi07TBmJCBqiTZ3jmKaYWx75VkpxQ1mjJblk8HG/tTnsxsnB3m3/jHU3jrJ08p\nvz/j/B3X7MsHzKUWTmwmmVDOIDfMHhreWTbSSU+G2W+sVMvsdJh97rNuSbYqw3zFcZQvhaxD2m7u\nuH8KD1wp+X5vudqCzYFjzrgKPwNTljFkdA3JHqTUKUUNudlR56yqhW473m42yuf4NwwLmaS6eRgg\nauJ3Au+76zz+5dSs8vvSqDw4kkHdsGH6BYKaQk6XD8gsNU0bGV1zz49/sMhCOtl2mCs+zmI+oHv5\nZmjXyHeOnAnj8D0+X8WN+/Mo5sTfH2VmqF3D3CXJjonD3J1h3ejZvxlq7vPPkWSnwhnTi9UWJvJJ\nT9O4PmeYk/0xpufLTfy3L5wLfe5Xaobb8AvwdiCO1iGTDl8upYGh9wyzN/DxpAN5ANE2z/OTZPdj\ntBYAvPvOi/jZD3wfU6Xe7Sa5Bw15M8y5pGtjRkH37zjq9IXodZ1lz2iyoT44zHXPnGxA7MMJFm2/\nAcm/nF7BGz/2cKhz1J1hzvVhn4sL5DAH0M7ObCTJ5p0Z5gDZ9kadMuUGqZIy7wTkZnZiLKdsztAw\nLBGI2GB+b1pPgDG2cYY5YGSUdIZzss7ZJ0NSceWHOlJJdadVuZFoCRbr+ssn5iuB0Vy5ye8bSm8o\nyc7oGlqmf8MiOVItkwy6P5ymXwl1DXNdSnk19T0kz39QhrlpWK5B0aukalC87dOP4Gf/6l7f78n6\n5aNOhtl3zJYng5XSmH8NsyfLk1SUjBj2xhlmb7ftdoOp9cffex5VQSfvdeSNnMeN//2Vc3jzJx5S\n1oPKe2hyWBhYfkZwzTDdsgZAkWE2xD6na8Ih9gsoNQzhVEtJdrvzbvsRfriYwbnFWs/1qxuhlmT3\nZgg1TRsXFmu48UDelb1Gaej6dclOBwT3tsKp6TL+08cfCZUZ7a7hlQ2VopZky2Z9AEI3lVp0Mszu\nPR2hEd6dYQf6l2H+zoUS7nhgFmcXqqF+TukwRxjY8ToUCcZEyVfPGWYTGT2Bv/qFp+CWQ8MAom2e\n17af2udoKB290we01YafPTXX88/IMo6xfPscFZ3RX1Htc2se2w8QQTeNqSfZdCNnoY9kkm6wIcpZ\nzN01wowJpWpQY9rN8rVzq7j7Ygl/8uUnenq/aXM0zM4MuCz/6vX47STIYQ7AbZijJTwNT/wl2b00\nPkpp3qZf/he7NML6cTNsF/JvODaWxVrD9M28iJq8RGBGUTrMgDCKNjtWqv1QEPNhDYuv22xlRD6f\n1h2n2v+Btuh0L79+Xx5zMa1htm2OuXITs6sNXycXEI4NY2KTVwUH6oYjkw7qEN917bcUcve0nkAi\nwZTSYZmZTDhGpSrLBnhqmH2cj6lSw23EFGdJtuq8SKTDfNyVZPvdH04XXid7v1HtsaqGWb5HT4g+\nDDbHunu283PEufZz0Jum5d6Lqnm43utINpSKM2fn/QNPcp87OCIcZr/rsS77AKTUNaJN03LvH1Uf\nhqYp7o92hlm8xyulfM7xEcyXW6FniKpwx0p1S7J7zDDPrDZgcSFxlLWhUUop/Zp+yeMXlTENiAZr\nv/ihB/Gt8ys4t9D7nlI3hTObYMx9TZT7RClL76xjz4dwmA3LRqluYqKQcpvGRTlWqnsONeCVpUdr\nTEvlwnK1d0eXc45S3exwmOXxi3pWttehEM5OrxlmCxOFFJ57ooihjFB7RZthXr+PDGd0lPuQvZS/\n40uPLfb8M7KLuVc2X8wmYXFEtkYZWD84IuwKxhhyqd4bn8ngwlBGx3BWB0e0gafusVKAsFP7kWGW\n9Dq+TB6jfNf64tQ4LkrIYQ7A2+Uyo2jIwjl3Gx8FNv2yOjPMftEXOVs2kxTjX+IiLQuLfBhKQ9JP\n3tRoia6veoKBMVWjNMutjU0nE4FjpWTdZJBTLTPM4rM73+fNMAc1jllxHshHx3KRNkiJksVqC4bF\nYdpcOYaiYVhu3bdqvnjVkUlnA8oRDJOLhnfOcfXL7rQs262fTSpmAQunWs5a9FcBuGOlZJdsn+vq\nyoowaEeyOi4shss2bCcbSd5kYOZogCS73VQooIbZI6V2a8i7HFnTK9tWlJV0dttWOd5ifNtGGWbZ\nF+LWoyOhZsZuJ96/7Rvn/OdqeiXZgL9EXRo7QXVnDdN2S35Ux00GD9dlmD3Z1dtOFAEAd1/0l/mH\npdI0obG2Q9oeK9Xbc0lmXopZHUXH4I1aki2ytp0Os83bjeyi4LHZdsAkzMiYpmG7wUZJIeL60GrL\n7OigWwgIzHQjnZFxJ3tXSGn9afrldZj7NIdZOi3LIRQMdacPgHdOdIKxyBvHVbscnmxS6/keKnvG\nHTHGMJ5PhgoKbIR/DXN/MszymTcTItEg/9buDDMQnVrl8fkqEgy4bl/OfS2b0nq+RmUQUDb9AqKr\ngTes9b0qAOGg9sMhlc+VmdVGT3to1afGHECogMNOghzmALyS7JTun2G2nDpnr0Hq57RJ2Xa7hmf9\nxSSzygeGMh1f7zTkMZp0DEm/jU12fWXOA8o3q+LMHgXEQ8ZvA6s7Y2wSCabMDHfIthUOgdtcIq3u\ntg0AK7UWhjM6ChldKasfNN7aam83bC81w0ImqQUqJ2pNC7mUHiy37h6p5pcJNWw3mJTSE74dXBum\n7RpWquMv1ziS1ZFQSKbktfYD107g8flKbIMapXqwrE5e6+N5IT/3Oz/eDM7GXbLbjoWqhtmbPe7O\nDsu9MOhzvF2BpbTbL9PXMm3csL+A5187jlqr9xE424n3ulH1Yaj0kGGuOfVn7cyfuukXAKUkvmEI\np1pLiPdVWusN3SPFDEYyeqgsaBDlhmjmwpwMaVrxDFSx5qntK7qS7GhrQ731y941Rtn4a8Fz/qdK\nvRv7sjbdy0Q+pWyEuRm65Zpt6f/G56jelR0qRJy1ak+5aJ+jlMagsegzzDI4E8Zh9j7zvQiHOdrm\neV4lTTbVu0NebrbHHQEiuxplt3mvQk8ynNHRNKNt0Ae0Eyd1w+65Vn6p2gIDMJLtzDAD0alVzsxX\ncWw021Un3LvDd3mljgQTGer2DOJo1uZ3fgBRetGPDLP8TIv3NgXGr08BIAMO8bSPtwI5zAF46//a\nklNV5kWdnZGvpZwup5lkwvdmlBv4fme25k6N0HRnmOVMZi8Nx2EDgmSIbUNSjJXyl1vLqLW6S3a7\nhjmlMKjcjrAp3Wkepu6SPZpL9S3CFwVeh1nVvKHREoGGTEBmuN25Wh3kceuTNYYEC2j65WaY/R27\npmm552Yjhzmji7mZvrW2jvP3guvGYHMom2oNmo2MHvm3SUPBV5LtHg+1w9zRJVsxB9voGCsl3mPa\nakl2SiHJds9Pst2bwC84IiX6+bQGzuPTMdeL1+BR1TOWGyaSGnODGiqHOZfWA5sxeSXZqi7PchLD\n+hrmztq2bEpdThKWiie7JT8/k+zd2O90mPvR9MtaZ0j2w2H2ZvTCZJi9QUDJRCGFhUqEx6B7tFgI\nSXb3WK58WnMdzyhoGuuNacYY0ooRkVtBOmNhsq8qOWlQk8vNsJUMc/c9OJZLRlvW0Oqc7w545uhG\nnGX2Zl0XeuzRs1wzUMzq7r4HtJ+JUe0lj89XcYPTUE2SS/nb6H6cna/ixFgWaT0ReYa54d6jnftI\nLoSsPwzlpoUTjqqtl8ZfLU/D0I71JROx6eIdJeQwB+CNkKqcYa8h2X5Yqx0LQB29khvUAUdyWovp\nBffeu87jZ953t/L78iaXzXB8M8xOzSogJLh+Dmp3DbNvl+yW5coSVV2yO2qYFU2lurttqzPMBkbz\nSXEOY2joA71lmOtdGeamIhiRT3my0KqRajrzKAX8z2PK4zCrMmi9BFCSGkMiIYJTQRLk55wYg5Zg\nuC8ieWrUbNRURh6jrCO39rv2G13OsM3X10b7Obrdx609Vko9K9tvnnP3Xiivj2wyuAGivK/DdvTd\nTnpxmOXYNTmXWjVmK+fcZwmmbvqV8UqyfY6ZaXORneuqYe42pIL2rrBUW1aHsSlNV2cAACAASURB\nVA4AWT3Rs1RRSo+HnDnhhbQWuSR7OxzmJccJu3Yih+nVcBnmbkn2RD4V6QSMqidgDLSdv16yT3U3\nwCXWeP2+PE7PVSJrWCT3g3RX0CCtscjLzeQ9uuwTnFchj1G+q49CJkJJNucc1S6nVzjMvR3jStPq\n7BAd8QziWtf1A7THK0U9WmqtYeKQUyccxmEedQKSEndWdgROqWHZmCo1ce14ruP1nKLJrB9n5qu4\nfr9wuKPOMPupNAARGIuyoz3gzKNu2XiyM77sai8Os9m2C7zk0xpqEQfF4kAkDjNj7AOMsXnG2MNR\nfF5ckAZhOmBsjjcL3TYS12+Gcg4zIDIwfg9012F2Msxxre9751fO4aGpNZQb6g7YQPvvWPHZ4KUk\nGxAyLZVhLY+pKrNRN6yO+j+/41o3LCSY+D0bnkenHjdIkl3MJpFLic7Rfg3N1uoG3vSRB/DlR+d9\nP6PfzK013GinqkFIw7A7ssd+x1bUMOvuefItNbDagSCVJL5lejPM/jXM3uCIKvDhrXNW1XrKnyvm\nkjg2mg2sY663LLfmebvZqP7KK6VW1e97u8i3G3FtvD+pJdlqubU7VirAqW64TpwW2M9B3tfubOIY\n7nPebLHKYRYdwT1jtnwMGJldYowp5yR79zmVw2tYNvREOxMkgwzdxm6UY5VEdqvLmUhqPc9h9maY\nAWHoRp0d6w4YqHqNbIXlWguFtIaT49lQ41Yahr3OWZwoJLHWMCM7R92SbFnn3ss91Z1hvvXoMEp1\nExd7nD+7EX5NvwCIDHPkDnP4DLPM0HVLslXB+c3QMG1YvDOLnQ2h0ig3zK6RSnoo2flGiP2p8/y4\nWdII71X5eddOCMd0sUeVxXK1c0420FZRRKHwkw341gUGe2zOV26YmF5t4sYuhzmqDHPT8r+H8in/\nZ8lWkDX91+3LQU+w3hxmj8LWi2huGL9A+FaJKsP8QQAvi+izYoPXiUorHsQtT+al1wyzKosgNyhZ\n+xtXya/koak139frTvZ4NCcig/4Z5rZcTeWgeqWKqrFSps1dA15tbIrGVIwxZebL+3DfOMOcag9n\n91nTg1dX8dXTC/hPH/s+FiLsaNkrS86okKGMrpTtrKth7rpmZQOn3AYZ5pbT9AtQH39ZjgCIa98v\nM9z0KA5UY72EYyE2ZlWtp9fRVAWmJG/8yAO4/Z3fHEgNrTdLoBrXJa9ZVbTbe8zaWV9VZrjtDHc7\nsVJ+3dlJW13DrCuafsk1ppPaBhlmy5Fkxz/DfGA4jVWFgSo7YBfckTydRpJp2WiZtuvUZlOa73QE\n2egRkDX+nceMcw7D4uLYd81hznVlWFN6dHLSStd8TUAduPRjrWEi65m/Pp6LNrvql2GW+0OUkt8l\nx2g/UsxgarXhG/Dzo2lavpJsQDRmjIJ6l9xXdvjtJcPc6Mow33pEjC164Kr/cz0sKoc5pZiCsBU2\n0/SrGiDJjqop2cPTZQDA8fGs+1qvkmzL5utUHqO5JBqGHVkZS81Yf4+7kuwIM5gt00bDtF2Heb7S\nm120XDM6Gn4B7TKUKGp4G55JE92/oxcF4UWnw/a1TsOwqKcBdI+mk/SjJFCe72I22XPjN5kc7F5f\nNsYli1shEoeZc/5vAJaj+Kw44R0rJR/E3Qagm3nRe+uSDaizbKtOxvaQdJhjaEgC7fU9dHXV9/sN\nx5AppIWc1M9hbnoyw0pHq0OSLd5jdzkXpmNIAuIcWDb3GYkjsjPydwHBSoEgh7lUa2Esl0ROykl9\nNgVvZ+qZEDVvUVGqGyjmRBa8pnjoNZz6P1X2WEYHc+l2h3g/I0LWVgLioaMaDybvDXUNs+122lZl\nqpvdGWbFNSO/r6ppl3znvNiyoq6n6wXvPeEnrWo58nNAlBL4PXwanqZCqsywnJMtstD+e5h0svUE\ng67MMDtZaF2dYfaO+dkow5zW242w4tiYTTrMR0ezSglkzTCdem3FPWR0GuSqa1bMpFcHnWS3Ujmn\nGQAqTSc72GXsRinJrrX8HeZejfW1htnRgXhyJN1TI5lekQ0fO9anmIKwFZaqBsbyKdx0sADD4nh8\nvrfu+94yE8k+R14aReCAc47VrmMM9J7B7M4wHxvNYCit4cxcNNMFVA5zJhmdCkIiHZTTc1V89Uxv\nY4vcUZJ9rGH++tllpDSG558cdV/L9uiQu31VPOc36nnm9Za9Lug2HKHkWSI/69BIBhk90XuG2bG3\nvMg5yVEok/w6uQOiMVsvDp9UNEw497W0exd6DAhsREuVYU5rqDatSOc9y2feUEYTDnkP/oc3Yeil\nXzXWg4ZqmANoudGdtsRQ7Wi1s5f+XbI9WTZF/eVaXTrMIhoZR6kiAPcmPRWYYRYyxGIu6dsRuO7J\njqWT6ppVb1Ye8DHkbdvNurTnBa93mL1ONeAzNqdDTeDf4bfeslA3bIzmUu3xGBs4zFF2RO2V1bqB\nkWxS6WgBjpwx1W7O1J09rnoyWKo6Z8650wnWa+yvf0+HJFv3l9/30iW7W9qtCkwlmHAuVBJ9oLMZ\n2CAcNq/B4/f7DYu717xqRIO3i7wryfapYfYGK+RrXmSASdcYgkZGAd2SbH+VRsabYVbNV0+2a5jj\nGImW0fYjxazScJRlDaqabrfDqXSYeyk10NcHlLwqAW+G2TtmShK1w9w9zmQorWOtsTmH+dBwGjNr\nzciMvO7sKtB+BkQp+V2uGRjPJ/G0wyID+9BUuaef85aZSGSGudcazo3WVWtZOFzMdLye7bH+snuO\ntRhblIqsC3PTtKGx9cZ0Wo9Wkm1YNmotUXYFAP/lU6fxlR6cZmlfdUuyM3p0kuxvnV/Bs48XO5t+\npXr7+2WGs+D52fZ4tui6MHcH3cLUwfeKbMo2nNWdxncbX/825yg3LIx0BYQYY8in9UiUSQ3PM8uL\naipLNzKYWvQ49ft6/Pt6Wp8iw1xIa+Dwtz83iztPOq33PCu8nVTsfA7le8zQ7zT0jd+ydWZmZmBZ\nO+PgmaaJ2dlZAMD8kjCq10rLWNRq0BLAymrZ/T4AzDgjPKrlVSzOcyTY+vcAQMMwYTYb4nXbQrlW\nX/ee6cWS2PT/f/beNFqy7CoP/O4Uc7yIeEO+eDnVKJVUUqk0Y6aWsQ1IAiOawRaWaSzkNst4NV6r\n6V62Me3uxtCNu1fjNkYgZqsZBBjMYIEECCQklaZSSSUpq7KGnPPle/HGmKc79o9zz71nvDcyX2ig\nqvafyop34sYdzjl37/19+9szgtzuHnbR6Xzl5TS6sbDG5f2BdA0A0BuO4RgROp0Oao6BzrF8P6au\nj8ibk88DH6OJL42ZzF2EvoVOp4P5lGS9b+x0UGdecJPpHBFAxkzImO3dDrfJDkYTWPH5jPtkTGf/\nEJ1iGtge9wewTQMH+3twZ+SZ3ri1y21Ue0Ny3aY/gRsHwjd291D2ecGIa3vd5N9Xdg7w0sZy5j47\nN7PsaDjD/eslzBGgN/aU3xnPXMB3MR6QOb67f4ROJXWUaPuU2XiIYY+c/95hF+yhqGPuzibodDow\noxD9MT+vqbM/n06SZz2eBtwYGlR78XHgexhPXem8B+MJzChEp9OBEQUYKdZQtz+EYxnxOvMwnofS\nmAudMf7p711O/v/qrQ6CZjHnri7XOt002XTt1h7MGe/09oajZM46RoDuUL7W/mgM2yDXNxmR4+3s\n7iEcpyIpvcEItknWx6BL2A6HR8fodNKgpdsj3+0eHmA0IHN/7+AQG1Zay3jUJfOkd3SYBIjHvT46\nnU4yL3f3yb417ncxidfKzv4+6lHaxxYAZi7ZC6dDsk5u7R+hs6Q1sizbPSTn1nQCTNwAN27tSEqg\n/fEM1YKJvb09FCxD2vdvxGvIm4zi9aHe92d+AH9OPg99F+Mpv2aHsWM4HY9weEB0EdwgQqNkotfj\nRe3MKMBk7kmf34mN3QBW5HPHqtoROv3ZQsfvjmYo20jGNhxS5nF15zDp/XsS2x/O8fBWmTsXd0rm\n73F/iKBeWcp9OBrN8dBmCcVggvWqjceuHeFN91dyvzeZ+zCF+1cIyB5746CP3ubJ3K+LHXKtq07A\n/YZjRhhO5rnX3h2Qd+F8MkIvIGt9pWhgfzBZyn3rj6Yo2PIctRBgPAuW8hsAcBy3KvveV2/gnrUS\nfvZjHfzhZ3fw2pz7exT7Av50hJ6Xrm0Ly1tDx2MXr2jzc9QIXMz9CMfdLkzD0H63E+/XkZeuNycg\nn20fdLFVOnnQPJy5aBaL3PnN4/2mNxwv7RntHJB7bfkzbFYtXDkY5R6bBlxG4EpjK46B7mh64vM7\nPCZryJ/zx7JCkozKO/7uMUmeme4Yvbjmt1Uysds7+bkBxJ8BAHc6Rq+XBvBGEL9fD7rYqJ18LwWA\nzhHxAwx/ioIZob/IHtIn7/bZZIxeL52PZuBh5oUYjSfwtreXcn5fbDt79mzumC9JwLy1tfWl+Jml\n2M2bN9FutwEAlQ6ZoKfbG2ivVVG0n4BTLCd/B4Bdl0yoU+tr2NpaR8F+Ak6JHwMAfvgkmis1tNtt\nVMs3YRqQxgRWFyslB/ec3QJwEXapIo35ctvMCzCP6xb2hh42NzeTPp3UImsHtXKIdruNleo1hKYj\nXYcbXEArvh/1yi0MZr40JsQzaNTIPVhvuQB20Vpbx3otDW5M+wZKtol2u42NbR/ADhqr64lCNwA4\nhQMUHRvtdhv7fh/AZVRXmmi3N9IxpT6K8XHWW3MAHbTWNrDC9P87jsiGcld7Pa6/vI5KvYV2O6Vb\nAcAo6OBMs4RbvRl8S54Ld2qdTmehYw3di9hs1WEO59juzZTfmYcX0Vqp4szmBoBLKNdWuHEDg2zU\np9ZXce50E8DTKFZr/Jg4u7reaqDdbqNWvgHDMrkxlOaz3lqJ5/42XD/kxtBsPj3OSm0PO6NAO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sKQSObnansE0DZ5plVDWZctdPkfJKUZ3tnvlB4kxkCXrRl61pkj6+ci/zFD2ma0m89ySAT1+K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w0Pe5hrWqltSPPwBRhWE5RYPntmPaH6uHXrFrrdch17Z8Vcw8Q0UKL98FTSLDu3slC6kuCn\nPjn7ObM9DIt2vYZut4tb/VdweblZ6vfWbVq43R9ErzkNbmGubh77sQGAZtsH8FWMPD0+RspdNGt6\n6fs3X9exO3Zh6SpWl4ofq/l2H14ANNtzsPdIw+XiShfdbnHpfRbmGjVsj8fR540Y0na558Gs2Nvb\nQ7fbxXKPXOBqrYFut4s7vSlsL8D1tW5p+3e97gJ4Gfuujp6tYKVtzfTac3UT00BDt9uF3yeF9tml\n8vavVTPgQYteL1DvomGWf44eFJahwVfj/Qluk/vK+lIX3W45MWNnFqcA7mBjQu6bF5bnCn/+lmXA\ngRptf3fo4uuuLhzo+6PnJYur6wqAGxgG5D7qbZGm+NmlOXS76TUzi8lkgtXV1cxt7idUQ5dSEVCZ\n0mDqIgjkM4IAYYSCQO4GaRkatgfZZlFWOJPYm7iYk8gZHxTQma5b+xO0BJI9RVFghdISanQjMv2i\n8p28GeZkZJFMkp1tDEbej/ytLIP5fgF1+MwylFIUYHdsR//mEbuuymc0i8XmhI7oGdFs9P1G4Qyz\nzH2cjV1baFTT0KsIWLm/XJKtwnY9jB1P6tjOR0YJzdvCexhdPIrm+ejPKNsp+v7pOUUl2bK5ti5z\nXO5nSTYA/MX3XMD7r6aLZSBpMiUbR2BHemSjOIZGZJAT5j5XNdMvQCDJnrrQlPLyPw+LOpNR6vkB\n3tydlJ7Typt+9UqOmzkMDE3FfF3H5jCWZMtGzQ4Les/qzCjTZGOFdqnUM2f0YRaQUaRYMp81gnUc\n4LOiqWN4medps6ajY+m405seyFRtoWlEs890lrXM+dW6qXKS7OpI5oF4rUgRjZ6UOMNM12X/938i\nCs1rBXOygaTDtu362Bo6WJek1RwEVNJPlWTxOfBg1ywiHOqsVBTlzymKcgPAewH8mqIov1nObh0P\n6KKwNyE3btFFq2tktmJ/TLcRLwAbpoad0EBJ5uppGRoG4WzAg37y0YLZD+T5enN1A/sTJ8phFkXM\nWAaZWY3clSXRRmSGOcP4KMyZjWc95UUbQB4a9zPog3lOshhRFAUNQ8NeNMOcPmeb/Ixmhguwk5HV\nTOf+7QxjMCDMEbWpI3d+BnZZ+Z3HjXnJMaKzVKMMz4PUDLMkgxwA9ifyexg9Jv1wG5FDOT0f7oaL\nKdl1zTZp7mfTrzxQ07mx7cGRzTAbTMEsSWIg29HGoA9NVaT+CceJdk3nXLI9tC29Miaglq5CAWlg\n3N6fwvWD0gvmtqWhP3VDhRTCfNbqNIWWWmZU7ABkhvko/FIuLZLvdda5xnrk9+IzLuvl7R8/wxyl\nB1TE9IuNIAWAm+HY0/mCWeNFsT5Xw639yUyRRRSXF+t4dWuEIAii9UH5M8xJl/AyY88Oi7qhJQpm\n6llQ5gwzfeb/3su7+MC1+cKxX0DSYZsWtWdK9GlYbtegKsDtcORo/wTPMB/qrhEEwa8GQXAuCIJa\nEASrQRB8vKwdOw7QxT69ccsu2pquRlFDMjdMy1Cxk8cwGyru9sk2D7rjHNsQkBbMloHe2I0zlkUu\n2aGjIjX9EhZkBWKl2jUdE8ePGx8yljM8B5Yr6q5cFHkMM0CaA3GslJgdUxRgaLuFcmaLumRnMsyO\nl1lUswVzUSOTqkOWRWzqWlgwu9J7EzX0mjrE3Ef0WrTR0c/IT6ZFNL0XikyC6MJ7a+hCVxXpfY5t\n0tzPsVJ5UMPvYGR7pKkhcR8HCNM1duSNj7pBYr0cPxAqbaqAtqWncpjLjpQ6DFRFiVQqlLm7VHLB\n3K0bcLwgWvD3Jq70+XYcWGqZnOmXPObyMLi2RKSps5qJsQUjZerLZOg7FlEAUGY5ywzxOECdoKl7\n+X5kqlVug/7ifB2vbo8O5EJ+damBvbGLnZGDG3sTqEq5KgDe9GviHo0K4qAgEYtswexBVcpVS7Hf\nJ82ULor5hoG9ETlvbocF81qJDLOuKlhp16JivDdxoaB8c8f7AdU5KysAerPPY49NTcVOVDBLGGZD\ni4pqeaxU7LDZecAZ5lWmsGnL3HQbBvbHDpPDLHZPnoTMCyB+QFNJ9sSR5/eeDXMIX90i5kYy92R6\nDpQZdXEcaJo6arqauVCoG1oUKyUqUKnbdm8sL6KKuWQzkmzXk8qt61HGslyS3bb0qOB7UApmGSh7\nPM5Y9Jq6Cs8PMLJd6ZgBPbaZDLMRj6cA8uuMHpdOXc4szp8Qhhkg19DOkMS3nOmmizNNVaIs+bHj\nSxeGlqFhbPvSSK8qoFXT0J8w7Oq0OnJkCsowvn4EUlcgbnTTRmN/6lWqabDcMuNZbpDCrMwMZopr\nK4QRY9+rCKhsfux46E9J461M9vex9RYCAC/cHoTvUy2GmXdyH069IxlruLrcwM296YFcyCnb+crW\nCP/uq9t4+sJcqQxwq5aMZqM51FUB75K9NbQx3zBKVdLMM4TZU+dmm9+fbxjR/efWPlEonJHEux4U\nS00jarz1py5aNQ1qRZRE9xLVuGtUBLVIkj0DwyxZIFimhp3wJM6aYaZ40EPAVxmJiJxh1rE/diK5\ntajQrZsaXD+IshVFBRkpqkkxZknye891yQ3llc2wYJY8QL0w1oWdlb0fcX2liUcEhkwsGmYsjZIV\nW01Tj2IFTD39vbIMsyynXFHCoiFioeVNp3EOw6woCpZbRDJ0v19DP/WJh/DjH7km/b0VFsz7Y1d6\n36EF7N7YkY4Z0G12h/L7UyzJdsO/ET8cI6l/hgdDgmG+z2eY89AwNby0QRbnFyTFmamrZM7ZlUsP\nKetSpQgcHnN1HV4Qz7lXTY4MxDm8b+6O0appudFgs4I2g/ZDZRSbp1sFsHJNAJnNtsPg+jJhmKkb\nflGwkuzBxEW7ppVaiDxxlpgSffEtYiiWFaV3HOBjpfpTF81a+WMN7Ezso2utmf6WZgL/zovbeHV7\njI8+LPZwOCja/DnqyhuJxwHqNURxpzfFWsnrQfaanNUHoNvQMQlHtW7vT6EAWO2USx40a1qk0qCj\nNycRJ/NTS8BLsi1TvuCMGGYJY9IwtKjYahjibVhW80FnmNkLTJrX2jDw/K0e+hMXTVOTzjADQG8s\nn62sm2poTCUvxijD/MrmIGR9xMeaMmxL97kk+4e+/gp+6OuvZG7DKiGkMmlmzjmLPaYFsyw/2WRM\n1+YbcoZ5ozfNNNcDSDNmZHuVyG08DL7v/Zcyf0+/g+2hjesr4kUPu42M6aLb7I5smLoqzjsPr5te\nhiQbIA/63ZGDdsZDnjYy6kY1sjWPEqRgJo7g5+fFBbOla5GKSSrJDvPFd4bVNbNbCe+JG/0pWfRO\nPVypmBKnYRIjyd7ExcX5eumFCJVS7o6dI5EUHxatmhaZnmmqgrHjoyFZ1xwGtAH9iUeXZ/q7qGAO\nJdmyEbeDotswcHWpgS++tQ/gfCStla0L7jVURUHdUCMCYBCyd2XjGjMT+/jZbGdjHsstE21Lw6+9\nQKI8n71UPAu9CDqWHq0FiPxZ7u1wHKibGka7McO80Zvi0mLxGeMiII1/E++7MruzNZ1/3hs7uN2b\nYrltlq5KIv42cRZ3le5x9xIn81NLQJmXaDEjk2TrKnZ3yTbyGeb4b6WSbKYIeNBnmNmFiqyDNmcR\nSfbeyJaaoNFjQhsWoplIKzSRmGTNvrZqMDQF+2NX6jgMxAXzcsUWgkcBVqonNeIyNeyFTtpZM8y2\nF2TKrU3GyVz2Xq2ajpcnQyL9zXgAPLLWrqxstUzQBtvd/lR6v6Df987Alj7UIoZ55EjPfauAJBuI\nJdZZDDMtKpoVkqoeFRqmhrBPigsL4kVVTVejppOcYSbOsUPbq6yZHZ2T2+jZuLbcDBnmah1jKsm+\n3ZtGbGOZoMY3+2Mnai5ViWGm65Ph1EOnrktn6w8LTVXw+b/xnpmLvViS7R+ZpP/h1Sa+dIMwzFRa\nWyUGk/gekP0aHJGk/1zYvGvVtJk9ERRFwdUl8h0amoJzJRuS0eulN3Gx3DIxtqulqmGN44IgwJ2+\njWcvlx959Tt/7d0H+juqmtkdkYK5TMMvijrjxN2fVsur4l6iOmdlBUDZyiKmXxQyaST7t3LTr/jn\nD7pLNgvZQ7FTNzB2fNzt21JTCfqg26IFs4Dhb4RzzlNHLAkGiEHP2XDGULYNwBbM9zfDXARFGOZm\nTcN+BsPMm37JCuaarua6ZK+0a7jbn8J2vUyG+bPf+DB+4Xufkv7+QQG7iJApUuj3tDW0pfcmlmGW\nFcy0OI9Nv8SLrLPh4imrGKYM84Muxwbi+1GrpsvdznU1im/LcsmeOD62B3ZlGea1NtkvajRTxYVU\nw9SwO3Zxe39auvMwEJ/buyM3Kpir9B3QApY6eR9VwQwQpuvApl+Oh/7UOxJ2dTl0Cg+CIDKpq9K9\niC3Ijoph1lUFP/9db8cvf/+TB/p7Ksu+MF8v3YSwzRTMAJVkV+f40NEwgHgUjGwPa0dQlB4U9B60\nNyL3uaPYN2rAClRz9OZe4bRgZhDnk2abfiUKZsnNjS0+ZDdntlCTyZQfRMg+K+3Wv7kzks6j0mNC\nM65FC37LIHPOg6mbWWjRrmuWPItKuJba1Vy0lolGUUn2mM61yk2/Jo4HP5B/t6auxi7ZkobFasfC\n1PVxd2BnNjV0TSwrftCwyJiayRps9B6WJcmm2+yOHGmhG80wT+XHGgD+/NPnAAAv3OpJ97thajA0\n5YE3/ALia+jCglz+W2PGGuQu2WQRvTOysVBRhpnGjdzpTeF4xGSxaguphYaJN7ZHCIBSs0kpOpYO\nBYRhps2lWWcQjxL0HjCYupi4PgJUq1iMcpht78gUCkstE7YXoD/1sBP6NsyaRXyUqJsse+dFztll\n4z2XulifO1jT6OoSWStdXizXNA9IMsx+EGDi+JVimGnyQRAE2AibgysV8rShjdntoY07vemBj3EW\nGqZa2SSAe4nqnJUVQCzJpgxztmkOIGY4+b+Vdcsoc9Cq6Q/8bB8QF1NZDDMAvLU7ljPMZizJVhVx\nYUe3IcZH8ocPZZizHDOfOE8cC7Mkpw8K2IWUrEBqmLGzu4hh1lQFihIbAckK71iS7UkzlmmnNGsW\n+iSBNZ6TPbBogyIIsljo2GhGVjDTGebBRH6sAeCDDy/jiTNN/K1vuC7db0VR0K0bR7YQrCLec3lB\n+ruarkauplL1kamhNyFF2GJF3d91lczdbfSn6NP53QqxqwCw3qkhDF04EuZFUxV0LB17Y7fSkuzB\n1IsWvEcxw3xQ0PN/7PhHxq4uh9fP1sDGzpCMoVTJhbkZGtMBRDpf9hx3GbgaxoZdLnl2F4ivl/7E\njQiKSjHM4ZjNxPWxEZraVYlhpgXz1zZHcP0AZ46gMVg3SGIJUWlUKwngXuJkfmoJYtOvYgxz09Sk\nhS5rEiUrPr7x7atYn7Nwtlt+R6iK+MXvfxf+9RdvShcULKssK5hp53F7aKNhit0k6TZ7IwcLGZ1k\nalQii5QCgF/4S0/h1v4E6gloaNCHlClxFgeABnOjFJ3XiqLA1FTGXVleeI/sbAfsNaZTKnudkwR2\nLEDKMDNu1lKGmdkmKyMeiNU2MgZfUxX893/uKtbWsrMjF5pmJReCZeNLb+0BAD708JJ0G8uIrw8Z\nk1I3VGyFKpqse9hxY71Tw+3eNDJhrNoMM5tHunpErFC3YWBv7ERqjHaFGkOsJJuymEcRK3VQWLoK\nBTRW6mgW4kstcv1sDmxsD+3SndIPi+WWiT+7RYwCBxUcawCAR9eaWG6ZeNfF2SKPioBlmCcVnDGn\nKsaR7eFun9yTVyukOGyHc+l/9OY+AHJPLhsNU0MAYGgTSXrV7vP3CifzU0tgRJLs7Bnm1U7+3J7M\nxZZ/v3eV7DhYZTx5vosnz8vNElhGLM/0a3toSxkrus3e2M4McKeS7Cz5T6duPPAO5hSUmc9ic9nF\nlqyIbdV0bA9JJ1ZWDM/VicFbZsHMZAmeBDlvHtgscJnsk1W85EVPAeKRBiA+B27ukVzHwxZtf/eT\nj5wIhvmjj67g//zCDTx9QX5fZ893WYIC++ypKsMMEKbl+dsD7IaqrPmK3SvZxeNRLXLn6yRqr/IM\nMy2YKyTJVhQFlqFiGM6GHkWxSA07twY2dkZOpeTYAPDQShO/+ZUt9Cdu6BReneNDMVc3DmxKlQd2\nhpmeo9WSZMdZ2TQqtkpNF0VR8NhaC//pVh8rbRNPni/f3JA2MDbChkGV7nH3EtU5KysA3vRLtpC/\nGLqfBkEgfa2HVmfLujtF0ol6XmJ0Y7EFc8b8H0Ck9VbGfPLZaIb59DIA4mI4gPy8Tsi2JYX1XF2P\n2DHZNt2Ggbv97MgotkB89opc4npSkDAJlIwIsHnnsi4wO1cua/qpqgJTV3Fjdwwg2bw4CJ69soB3\nnC2fnagafvqbHsEXP/vhTEUE+/3LFobWfVIwX1yo49b+JJrtkxmdHRcoq9yuaUfm0r7WqeHVrTF6\nExeGplTqeUJnygcMw1wluStA9uff/NkGALknzGFAr5+toY3toYOFil1PD60QufPzt/tw/eBEKHFY\nUEVGb+JG5lpVOkfZrOydoYNGxST9APDffusj+ND1Bfyj/+zhIzl/6NqQ3uerqIK4F6jOnb0CUBQF\nRii9tQxVKsO9HDoGUqdmEc5JMjhPIQdrViCLzaE30iDImB8Pb3CeH2SbfkUzzNW6+R0Xnrk0j0dW\nW/iOp85Ktyky59y2DGz2sxnmbsOM5oFk27Ay4KculB/jcD9DpnpYZQpb2UONvbayItVo8TPfMCq3\nQKgqdE3NlaslGOacph+Ayi3wWVxZasAPEMX2dBvVWkitdch3d5QmPe+9Mo/NgY0/frNHTMBKzno+\nDFiG+eY+UYtUyZQMAHZGTjQDL1PFHAatmgZLV7E5sLEztCvJMAPAF98i11AVGeajhKGpaJhaZWeY\nI0m242N35FSKXaZYbdfwT7/9MTx94Wia0vQ5dTckQqpm7nivUK07ZwWgawocP8i8YC8vkhtcBsF8\nIky8yoaiKFht17DRn6IrYZjZ2Za8+Uv+/3kstUzUdDXTgfkk4ZmL8/jcj70vcxuWpZGxxx1Lxwu3\n5FnNAJFu0usnq6nx/e+/iMWWeXo9cZA1lNjvUla4NUwdHUtHb+Jmsm5Xl5vY6E0PzS6fIgm2+SBr\nRLD3rdUKGczwuLpMmsd0sS9LNzguWEnCCAAAAA3jSURBVIaG+YZxpDOHX3eFyO//7FYflxaq1Siv\n6SoMTUF/6uJX/3QPZ+ZqeNt6tdRv8w0DuyMH3/nkGj5wrXwlkaIoWGqZ2Ojb2Bu7lSt4Vtsm2jUN\nf3rzZBbMAKLnEWWYKyXJptFnUyLJrpqK5l6gzjHMJ1WSfTI/dQZMTcHYkS9kABIZUgT/w3e9M+qY\nnaIYLi01sNGfQhL7Wiiuq56Ys5UfR0VR8PSFbuQAeYp81AvMMHfqBlyfVMNyhjl+6GQdo7/ziYcP\nspsPPIp04LOYzsWmSQrmDIb52nILf/DKTqYPwClmRxGGmf5cVeRKmirg4nwdmgK8dHeImq5WyqyH\n4rufWY8MHo8CSy0TV5YaeHVrVMmFZKum482dCf7jG/v44ecuQK0QAw4A/+r7noClq1LfkjKw0DDw\n2haJF6vaiIOiKFhomrgR+kWcNEk2QCJFt4dOJRnmRi3OCt8dOZVyyL5XqIdz3Lf2ScFctcbovcLJ\nuzJzQEPZsx781FX56RyZ6MceWy1vx04IfvbTb8M/+PUX8fRFsWmOVWD+kr3Z5s2T/W+feeYAe3ly\nkZRkixde7KJRVlSzBXNW0XaKJH7g6y7hV/7kZqbs09AUOF6Adk3+UGuHDHUjh2E+Rfmg7IkiicUj\n25Br4ky3WowlD1NXcX6+jtd3xphvGJWSI1P81fdfOPL3uLxYx6tbo0q6x7ZrGp6/TVyYHz/TPua9\nSeOo3MtZzDcMfPnVAYBqZTBTdCwdXw6PURXPoaPG5cUGvnSjh/3QP6hKLHvEMIemX4+uVUuhcS9A\n1320qVM1lca9wsm7MnNAjb/yZva++NkPnWbDHgEuLDTw89/zlPT3qkrkVVuDDNMvk5U8nh6jMtEs\nYPrVsVj2OL9gvnzK8BfGT378Ifzkxx/K3KZV07E7cjIXXrSYzmpW0Li7YThfeIpyQK+JuqFJC0x6\nbR0lM1oWHllt4vWdMboVm429l6BS7CplHFO0ajre3CXF4oUT6q0y34hVT6y5aFXQrmlRXvhJvI6u\nLTfwG1/exIsbAyhApaJWabE4mBKGucoxf0cFSkLd2p9AU6rng3CvUL27+zGDLiDzJCFty8jM7z3F\n0YEaqslnmIszzKeYDY+fi00l5JLs+GYqM56ar8eLFmqid4py8OFHlgFkG+hQFYCe0fR74nwXHUvH\nj3zwSrk7eMJBVTLnMtjjnRHxAKg6wwwAj1VsJvY4cHmRHKf9MF6rSjg3T4oPXVVO7HgFO3e6XKEM\nXQpWlXWU0vSq4mq4Bvj8V3ewPler1LqN1gR3+1O4fnAiZ5jpWvvW/hQLTbNyYx33CtU5KyuCpbB7\ndOoKW13QhabUJZthlU8Ni8rFcruGZy6SUYSG5BphGebzC+JimGWYT6+1cvH3PvUYPvej782c1aNN\njcFUvsCfqxv4489+GO+/ulj6Pp4CeDLD0fT9VxehKMBffu/Ry4kPi8dCieLtcL7tJIJGTW4PnWPe\nkzSeDnNZFSUeOTtpmGfc25cqNsMMJGXYVZyDP2pcC80D39qbVE4FYWgKdFWJXOZPohyZXVOfxM9P\ncfKuzBwst8jJUEXzklMQLIczT9L5P2bO+eLiKXtZNv73zzyDt3bGUoUF6+BcxPTrFOXC1FU8spY9\nq/jRR1fwr/7oJh5br95M44OOF++QWcUnzss9MC4sNPDVv/exe7VLh8Ijq6RgzsqeftBxcYE0Zt91\nsXpZ4zRqxvEyYj0ecNBF/nxdr+R5SgvmVk07kU2Ns10rcsq+WDGneUVR0DA1fOXOEACwfhJNv5i1\n3klk2ClOC2YOy818qeIpjhf04debiLv5bH72pdOCuXQYmoorGYZQ7ZBhzpqhpTKnb3h0pdydO0Uh\nfPChZfzJ3/3wiXRkPW78wNddwmvbI3zsATn325aOn/7Ga3jn2ZPbfJmrG/iNH3mmkvOxVO76bU+c\nXBNSusg/yjzuw4CyyrK4wAcdqqLgk29bxi/98e1KMuxNU8ONvQl0ValcLNu9gKYqqBsqxo5/Ime4\nKap3Zh4zqCS7P6neLNIpCOZDSdXOKF/+VtUH5IMM6nCZtXhUFAV/+JNfHxXXp7j3OC2WjwePn5vD\n5370vce9G6Xi255YO+5dOHacmavm+I+qKP9/e/caY1dVBXD8v5hpO7TFzpRHCx0exSLYVLGkQhHF\nQg2iIqghWoIBeUSNJCJRCcgH4gc+GIyoQZGEh2gMQioRoolakUS+UBTxgaCCiBRogVimGhA7LcsP\nZ09nrLcdJjO95zDn/0uauWefnds9k3XXveuevffh/s+/bceGpm00f3b1XtTELzRgdK+PeS1+P7zo\nHYey+cVhTn9T875IPH5xP3f87lkOGehr7RKyoxe9jvueGGr1lGwvo+5k/zm7v3qp+q0+an8GZs/g\nnOPGX9/XxNucTHcjRfAJS/bbbb99585q5PQ4SZpOZvXu1dqNemB0DXMTN/wCrzBDta/G1R88ikMa\nNiUb4KwVBwGjy0/a6JiyF8LWba/UPJL6tPfVuQsja5i3/NuCuan2mzuL+y8/adx+u9v0SHvO6/ef\nw60XvpWjB5u3nk+S1C4Ds2fQE7CwoetP9+mrrlrOa+B0ZMEbDpjD9WuW7djgsI1OXDKfb977ZKuX\nOU7q1RkRVwPvB7YCfwXOy8yhqRhYXfZt8XSD6WT9Zau8elmjFYcO1D0ESZLom9HDdWuWceQBu957\no06jV5j9/NlUKxfvepPGNnjjwrn8+JMrOLClt6aDyU/JXgcsy8w3A38BLp/8kOrVv3cPl6xewvVn\nL697KJqE+XNmukZTkiRx3GH9jb07w441zC2ekq3mGxzoo6eFu7iPmNSrMzN/NubwPuDMyQ2nfhHB\np1YdXvcwJEmSNM0NzJ5B717R2CnjkqZ2DfP5wG1T+HySJEnStLVPXy+3X7Ccg/ubudO6JIjM3d/M\nPiJ+DnS6Z8QVmXln6XMFsAL4UHZ4wo0bN+b27dunYLh73vDwML29TotR82zbts3YVOMYl903PDxM\nT087b2/yam3fvt2/kRrJ2FQTTXVcZuZr5rPB4ODguHPNxy2Yx32CiI8BnwBWZ+ZLu+g2uf+kizZs\n2MDChd5TUs2zadMmY1ONY1x23zPPPEN/f7s3oRnP0NCQfyM1krGpJprquHz55ZdZsGDBlD3fHjZu\nwTzZXbJPBS4F3rmbYlmSJEmSpNecye6SfS2wD7AuIn4bEd+agjFJkiRJklS7ye6SvWSqBiJJkiRJ\nUpNMeg2zJEmSJEnT0WSnZEuSJEmSNC1ZMEuSJEmS1IEFsyRJkiRJHVgwjxERp0bEnyPisYi4rO7x\nqD0i4uCIuCciHo6IP0bExaV9fkSsi4hHy8+B0h4R8fUSq7+PiGPq/Q00nUVET0Q8GBE/KseLI2J9\nib/bImJmaZ9Vjh8r5w+rc9ya3iKiPyLWRsSfIuKRiDjenKm6RcQl5X38oYi4NSL6zJmqQ0TcFBHP\nRcRDY9omnCMj4tzS/9GIOLeO36VuFsxFRPQA3wDeAywFzoqIpfWOSi2yDfhsZi4FVgIXlfi7DLg7\nM48A7i7HUMXpEeXfx4Hruj9ktcjFwCNjjr8EXFPulPACcEFpvwB4obRfU/pJe8rXgJ9k5lHA0VQx\nas5UbSJiEfBpYEVmLgN6gDWYM1WPbwOn7tQ2oRwZEfOBK4HjgGOBK0eK7DaxYB51LPBYZj6emVuB\n7wNn1DwmtURmbszM35TH/6L64LeIKgZvKd1uAT5QHp8BfCcr9wH9EXFgl4etFoiIQeB9wA3lOICT\ngbWly85xORKva4HVpb80pSJiHnAicCNAZm7NzCHMmapfL7B3RPQCs4GNmDNVg8z8JbB5p+aJ5sh3\nA+syc3NmvgCs4/+L8GnPgnnUImDDmOOnSpvUVWVK1nJgPbAgMzeWU5uABeWx8apu+SpwKfBKOd4X\nGMrMbeV4bOztiMtyfkvpL021xcDzwM1lucANETEHc6ZqlJlPA18GnqQqlLcAD2DOVHNMNEeaO7Fg\nlholIuYCPwA+k5n/HHsuq5ume+N0dU1EnAY8l5kP1D0WaSe9wDHAdZm5HHiR0amFgDlT3Vemqp5B\n9YXOQcAcWng1Tq8N5shXz4J51NPAwWOOB0ub1BURMYOqWP5eZt5Rmp8dmTZYfj5X2o1XdcMJwOkR\n8QTVMpWTqdaN9pfphvC/sbcjLsv5ecA/ujlgtcZTwFOZub4cr6UqoM2ZqtO7gL9l5vOZOQzcQZVH\nzZlqionmSHMnFsxj/Qo4ouxkOJNqk4a7ah6TWqKsWboReCQzvzLm1F3AyI6E5wJ3jmk/p+xquBLY\nMmaKjTQlMvPyzBzMzMOocuIvMvNs4B7gzNJt57gcidczS3+/vdaUy8xNwIaIOLI0rQYexpypej0J\nrIyI2eV9fSQuzZlqionmyJ8Cp0TEQJlBcUppa5XwdTkqIt5LtV6vB7gpM6+qeUhqiYh4O3Av8AdG\n14p+gWod8+3AIcDfgQ9n5ubyRnwt1VSvl4DzMvPXXR+4WiMiVgGfy8zTIuJwqivO84EHgY9m5n8i\nog/4LtUa/M3Amsx8vK4xa3qLiLdQbUY3E3gcOI/qQoA5U7WJiC8CH6G6+8WDwIVUaz7NmeqqiLgV\nWAXsBzxLtdv1D5lgjoyI86k+kwJclZk3d/P3aAILZkmSJEmSOnBKtiRJkiRJHVgwS5IkSZLUgQWz\nJEmSJEkdWDBLkiRJktSBBbMkSZIkSR1YMEuSJEmS1IEFsyRJkiRJHVgwS5IkSZLUwX8BGY0fA/hI\n+BYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yt3TIQ6EYysS",
        "colab_type": "text"
      },
      "source": [
        "## Hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FNw-tcLPYysS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "RNN_CELLSIZE = 32   # size of the RNN cells\n",
        "SEQLEN = 16         # unrolled sequence length\n",
        "BATCHSIZE = 32      # mini-batch size\n",
        "LAST_N = SEQLEN//2  # loss computed on last N element of sequence in advanced RNN model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bzLvRzgjYysU",
        "colab_type": "text"
      },
      "source": [
        "## Visualize training sequences\n",
        "This is what the neural network will see during training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IxTQ2-BRYysV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "picture_this_2(data, BATCHSIZE, SEQLEN) # execute multiple times to see different sample sequences"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idWk7K3FYysn",
        "colab_type": "text"
      },
      "source": [
        "## Prepare datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOmIaCtYyso",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# training to predict the same sequence shifted by one (next value)\n",
        "labeldata = np.roll(data, -1)\n",
        "\n",
        "# cut data into sequences\n",
        "traindata = np.reshape(data, [-1, SEQLEN])\n",
        "labeldata = np.reshape(labeldata, [-1, SEQLEN])\n",
        "\n",
        "# make an evaluation dataset by cutting the sequences differently\n",
        "evaldata = np.roll(data, -SEQLEN//2)\n",
        "evallabels = np.roll(evaldata, -1)\n",
        "evaldata = np.reshape(evaldata, [-1, SEQLEN])\n",
        "evallabels = np.reshape(evallabels, [-1, SEQLEN])\n",
        "\n",
        "def get_training_dataset(last_n=1):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(\n",
        "      (\n",
        "          traindata, # features\n",
        "          labeldata[:,-last_n:SEQLEN] # targets: the last element or last n elements in the shifted sequence\n",
        "      )\n",
        "  )\n",
        "  # Dataset API used here to put the dataset into shape\n",
        "  dataset = dataset.repeat()\n",
        "  dataset = dataset.shuffle(DATA_LEN//SEQLEN) # shuffling is important ! (Number of sequences in shuffle buffer: all of them)\n",
        "  dataset = dataset.batch(BATCHSIZE, drop_remainder = True)\n",
        "  return dataset\n",
        "\n",
        "def get_evaluation_dataset(last_n=1):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(\n",
        "      (\n",
        "          evaldata, # features       \n",
        "          evallabels[:,-last_n:SEQLEN] # targets: the last element or last n elements in the shifted sequence\n",
        "      )\n",
        "  )\n",
        "  # Dataset API used here to put the dataset into shape\n",
        "  dataset = dataset.batch(evaldata.shape[0], drop_remainder = True) # just one batch with everything\n",
        "  return dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q443hxmJ5LNe",
        "colab_type": "text"
      },
      "source": [
        "### Peek at the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FN027ePq5Kxy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_ds = get_training_dataset()\n",
        "for features, labels in train_ds.take(10):\n",
        "  print(\"input_shape:\", features.numpy().shape, \", shape of labels:\", labels.numpy().shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SGh2-0E6YysZ",
        "colab_type": "text"
      },
      "source": [
        "## Benchmark models\n",
        "We will compare the RNNs against these models. For the time being you can regard them as black boxes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVNz_D8bd8lA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# this is how to create a Keras model from neural network layers\n",
        "def compile_keras_sequential_model(list_of_layers, model_name):\n",
        "  \n",
        "    # a tf.keras.Sequential model is a sequence of layers\n",
        "    model = tf.keras.Sequential(list_of_layers, name=model_name)    \n",
        "    \n",
        "    # to finalize the model, specify the loss, the optimizer and metrics\n",
        "    model.compile(\n",
        "       loss = 'mean_squared_error',\n",
        "       optimizer = 'rmsprop',\n",
        "       metrics = ['RootMeanSquaredError'])\n",
        "    \n",
        "    # this prints a description of the model\n",
        "    model.summary()\n",
        "    \n",
        "    return model\n",
        "  \n",
        "#\n",
        "# three very simplistic \"models\" that require no training. Can you beat them ?\n",
        "#\n",
        "\n",
        "# SIMPLISTIC BENCHMARK MODEL 1\n",
        "predict_same_as_last_value = lambda x: x[:,-1] # shape of x is [BATCHSIZE,SEQLEN]\n",
        "# SIMPLISTIC BENCHMARK MODEL 2\n",
        "predict_trend_from_last_two_values = lambda x: x[:,-1] + (x[:,-1] - x[:,-2])\n",
        "# SIMPLISTIC BENCHMARK MODEL 3\n",
        "predict_random_value = lambda x: tf.random.uniform(tf.shape(x)[0:1], -2.0, 2.0)\n",
        "\n",
        "def model_layers_from_lambda(lambda_fn, input_shape, output_shape):\n",
        "  return [tf.keras.layers.Lambda(lambda_fn, input_shape=input_shape),\n",
        "          tf.keras.layers.Reshape(output_shape)]\n",
        "\n",
        "model_layers_RAND  = model_layers_from_lambda(predict_random_value,               input_shape=[SEQLEN,], output_shape=[1,])\n",
        "model_layers_LAST  = model_layers_from_lambda(predict_same_as_last_value,         input_shape=[SEQLEN,], output_shape=[1,])\n",
        "model_layers_LAST2 = model_layers_from_lambda(predict_trend_from_last_two_values, input_shape=[SEQLEN,], output_shape=[1,])\n",
        "\n",
        "#\n",
        "# three neural network models for comparison, in increasing order of complexity\n",
        "#\n",
        "\n",
        "# BENCHMARK MODEL 4: linear model (RMSE: 0.215 after 10 epochs)\n",
        "model_layers_LINEAR = [tf.keras.layers.Dense(1, input_shape=[SEQLEN,])] # output shape [BATCHSIZE, 1]\n",
        "\n",
        "# BENCHMARK MODEL 5: 2-layer dense model (RMSE: 0.197 after 10 epochs)\n",
        "model_layers_DNN = [tf.keras.layers.Dense(SEQLEN//2, activation='relu', input_shape=[SEQLEN,]), # input  shape [BATCHSIZE, SEQLEN]\n",
        "                    tf.keras.layers.Dense(1)] # output shape [BATCHSIZE, 1]\n",
        "\n",
        "# BENCHMARK MODEL 6: convolutional (RMSE: 0.186 after 10 epochs)\n",
        "model_layers_CNN = [\n",
        "    tf.keras.layers.Reshape([SEQLEN, 1], input_shape=[SEQLEN,]), # [BATCHSIZE, SEQLEN, 1] is necessary for conv model\n",
        "    tf.keras.layers.Conv1D(filters=8, kernel_size=4, activation='relu', padding=\"same\"), # [BATCHSIZE, SEQLEN, 8]\n",
        "    tf.keras.layers.Conv1D(filters=16, kernel_size=3, activation='relu', padding=\"same\"), # [BATCHSIZE, SEQLEN, 8]\n",
        "    tf.keras.layers.Conv1D(filters=8, kernel_size=1, activation='relu', padding=\"same\"), # [BATCHSIZE, SEQLEN, 8]\n",
        "    tf.keras.layers.MaxPooling1D(pool_size=2, strides=2),  # [BATCHSIZE, SEQLEN//2, 8]\n",
        "    tf.keras.layers.Conv1D(filters=8, kernel_size=3, activation='relu', padding=\"same\"),  # [BATCHSIZE, SEQLEN//2, 8]\n",
        "    tf.keras.layers.MaxPooling1D(pool_size=2, strides=2),  # [BATCHSIZE, SEQLEN//4, 8]\n",
        "    # mis-using a conv layer as linear regression :-)\n",
        "    tf.keras.layers.Conv1D(filters=1, kernel_size=SEQLEN//4, activation=None, padding=\"valid\"), # output shape [BATCHSIZE, 1, 1]\n",
        "    tf.keras.layers.Reshape([1,]) ] # output shape [BATCHSIZE, 1]\n",
        "\n",
        "# instantiate the benchmark models and train those that need training\n",
        "steps_per_epoch = steps_per_epoch = DATA_LEN // SEQLEN // BATCHSIZE\n",
        "NB_BENCHMARK_EPOCHS = 10\n",
        "model_RAND   = compile_keras_sequential_model(model_layers_RAND, \"RAND\") # Simplistic model without parameters. It needs no training.\n",
        "model_LAST   = compile_keras_sequential_model(model_layers_LAST, \"LAST\") # Simplistic model without parameters. It needs no training.\n",
        "model_LAST2  = compile_keras_sequential_model(model_layers_LAST2, \"LAST2\") # Simplistic model without parameters. It needs no training.\n",
        "model_LINEAR = compile_keras_sequential_model(model_layers_LINEAR, \"LINEAR\")\n",
        "model_LINEAR.fit(get_training_dataset(), steps_per_epoch=steps_per_epoch, epochs=NB_BENCHMARK_EPOCHS)\n",
        "model_DNN = compile_keras_sequential_model(model_layers_DNN, \"DNN\")\n",
        "model_DNN.fit(get_training_dataset(), steps_per_epoch=steps_per_epoch, epochs=NB_BENCHMARK_EPOCHS)\n",
        "model_CNN = compile_keras_sequential_model(model_layers_CNN, \"CNN\")\n",
        "model_CNN.fit(get_training_dataset(), steps_per_epoch=steps_per_epoch, epochs=NB_BENCHMARK_EPOCHS)\n",
        "\n",
        "# evaluate the benchmark models\n",
        "benchmark_models = [model_RAND, model_LAST, model_LAST2, model_LINEAR, model_DNN, model_CNN]\n",
        "benchmark_rmses = []\n",
        "for model in benchmark_models:\n",
        "  _, rmse = model.evaluate(get_evaluation_dataset(), steps=1)\n",
        "  benchmark_rmses.append(rmse)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CuNpxRbP_QYQ",
        "colab_type": "text"
      },
      "source": [
        "## RNN model [WORK REQUIRED]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XEyJklu-Yysi",
        "colab_type": "text"
      },
      "source": [
        "1. Train the model as it is, with a single dense layer (i.e. a linear model). Execute the Evaluation and Prediction cells and compare to benchmark models.\n",
        " * How good is it ?\n",
        "1. Implement an RNN model with a single layer GRU cell: [`tf.keras.layers.GRU(RNN_CELLSIZE)`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU)<br/>\n",
        " followed by a linear regression layer (readout): [`tf.keras.layers.Dense(1)`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense)<br/>\n",
        " Note down the shapes at each layer and adjust as necessary with [`tf.keras.layers.Reshape([…], input_shape=[…])`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Reshape)<br/>\n",
        " Hints:\n",
        " * The first layer must have an `input_shape=[…]` parameter\n",
        " * tf.keras.layers.GRU implements an unrolled sequence of GRU cells. Its expected input shape is therefore [BATCHSIZE, SEQLEN, 1]\n",
        " * By default, tf.keras.layers.GRU only returns the last element in the output sequence. Its default output shape is [BATCHSIZE, RNN_CELLSIZE]\n",
        " * A dense layer with a single node has an output shape of [BATCHSIZE, 1]\n",
        " * Compare this with the shape of input and output data in the  \"Peek at the data\" cell above.\n",
        " * In Keras, tensor shapes have an implicit first dimension of BATCHSIZE. When you write input_shape=[SEQLEN,], the real shape is [BATCHSIZE, SEQLEN]\n",
        " * Pen, paper and fresh brain cells <font size=\"+2\">🤯</font> strongly recommened. Have fun ! \n",
        "\n",
        "1. Now add a second layer of GRU cells. The first layer must feed it the entire output sequence.\n",
        " * The syntax for that is  [`tf.keras.layers.GRU(RNN_CELLSIZE, return_sequences=True)`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU)\n",
        " * You can try three layers too but it will not perform significantly better.\n",
        "1. Another option for RNNs is to compute the loss on the last N elements of the predicted sequence instead of the last 1.\n",
        " * Copy your RNN model and rename it model_RNN_N\n",
        " * Modify the last GRU layer to output the full sequence with  [`tf.keras.layers.GRU(RNN_CELLSIZE, return_sequences=True)`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU)\n",
        " * The readout regression must now be replicated on all outputs in the sequence. The syntax for that is [`tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(1))`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/TimeDistributed)\n",
        " * Keep only the last N values in the sequence. This can be done with a lambda layer: [`tf.keras.layers.Lambda(lambda x: x[:,-LAST_N:SEQLEN,0])`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Lambda)\n",
        " * This model requires different target values during training and evaluation. Use get_training_dataset(last_n=LAST_N) and get_evaluation_dataset(last_n=LAST_N) to get them. Check shapes in the \"Peek at the data\" cell above.\n",
        " * A good value to try is LAST_N=SEQLEN//2. You can now train, evaluate, predict. Was is worth the extra effort <font size=\"+2\">🤯</font>?\n",
        "1. (Optional) In the \"Benchmark\" cell at the end of the notebook, copy-paste the layers of your RNN and RNN_N implementations and run the bechmark.\n",
        " \n",
        "![deep RNN schematic](https://googlecloudplatform.github.io/tensorflow-without-a-phd/docs/images/RNN1.svg)\n",
        "<div style=\"text-align: right; font-family: monospace\">\n",
        "  X shape [BATCHSIZE, SEQLEN, 1]<br/>\n",
        "  Y shape [BATCHSIZE, SEQLEN, 1]<br/>\n",
        "  H shape [BATCHSIZE, RNN_CELLSIZE*N_LAYERS]\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPsQiS8pYysi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_layers_RNN = [\n",
        "    \n",
        "    tf.keras.layers.Dense(1, input_shape=[SEQLEN,]) # input shape needed on first layer only\n",
        "    #\n",
        "    # TODO: Replace the dummy Dense layer with your own layers\n",
        "    #\n",
        "]\n",
        "\n",
        "model_RNN = compile_keras_sequential_model(model_layers_RNN, \"RNN\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xKoWDXEC_InP",
        "colab_type": "text"
      },
      "source": [
        "## Training loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtqXxnUHhTHi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# You can re-execute this cell to continue training\n",
        "\n",
        "NB_EPOCHS = 3      # number of times the data is repeated during training\n",
        "steps_per_epoch = DATA_LEN // SEQLEN // BATCHSIZE\n",
        "\n",
        "model = model_RNN # model to train: model_LINEAR, model_DNN, model_CNN, model_RNN, model_RNN_N\n",
        "train_ds = get_training_dataset() # use last_n=LAST_N for model_RNN_N\n",
        "\n",
        "history = model.fit(train_ds, steps_per_epoch=steps_per_epoch, epochs=NB_EPOCHS)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "br7nVK9ijnCU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9RkMeEXW_qQ",
        "colab_type": "text"
      },
      "source": [
        "## Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GyOzPv8Znu61",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Here \"evaluating\" using the training dataset\n",
        "eval_ds = get_evaluation_dataset()  # use last_n=LAST_N for model_RNN_N\n",
        "loss, your_rmse = model.evaluate(eval_ds, steps=1)\n",
        "\n",
        "picture_this_hist_yours(benchmark_rmses + [your_rmse])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJeFuhNRXCmq",
        "colab_type": "text"
      },
      "source": [
        "## Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpewCYd2Yys3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# execute multiple times to see different sample sequences\n",
        "subset = np.random.choice(DATA_LEN//SEQLEN, 8) # pick 8 eval sequences at random\n",
        "\n",
        "predictions = model.predict(evaldata[subset], steps=1) # prediction directly from numpy array\n",
        "picture_this_3(predictions[:,-1], evaldata[subset], evallabels[subset], SEQLEN)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ScB9vM_Yys6",
        "colab_type": "text"
      },
      "source": [
        "<a name=\"benchmark\"></a>\n",
        "## Benchmark\n",
        "Benchmark all the algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EUH_ZsZKh1D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "your_RNN_layers = [\n",
        "    #\n",
        "    # copy-paste your RNN implementation here (layers only)\n",
        "    #\n",
        "]\n",
        "assert len(your_RNN_layers)>0, \"the model has no layers\"\n",
        "your_RNN_model = compile_keras_sequential_model(your_RNN_layers, 'RNN')\n",
        "\n",
        "your_RNN_N_layers = [\n",
        "    #\n",
        "    # copy-paste your RNN_N implementation here (layers only)\n",
        "    #\n",
        "]\n",
        "assert len(your_RNN_layers)>0, \"the model has no layers\"\n",
        "your_RNN_N_model = compile_keras_sequential_model(your_RNN_N_layers, 'RNN_N')\n",
        "\n",
        "# train your models from scratch\n",
        "your_RNN_model.fit(get_training_dataset(), steps_per_epoch=steps_per_epoch, epochs=NB_BENCHMARK_EPOCHS)\n",
        "your_RNN_N_model.fit(get_training_dataset(last_n=LAST_N), steps_per_epoch=steps_per_epoch, epochs=NB_BENCHMARK_EPOCHS)\n",
        "\n",
        "# evaluate all models\n",
        "rmses = []\n",
        "benchmark_models = [model_RAND, model_LAST, model_LAST2, model_LINEAR, model_DNN, model_CNN]\n",
        "for model in benchmark_models:\n",
        "  _, rmse = model.evaluate(get_evaluation_dataset(), steps=1)\n",
        "  rmses.append(rmse)\n",
        "_, rmse = your_RNN_model.evaluate(get_evaluation_dataset(), steps=1)\n",
        "rmses.append(rmse)\n",
        "_, rmse = your_RNN_N_model.evaluate(get_evaluation_dataset(last_n=LAST_N), steps=1)\n",
        "rmses.append(rmse)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZpYqIBnZcq_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "picture_this_hist_all(rmses)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VgeUgL-Yys8",
        "colab_type": "text"
      },
      "source": [
        "Copyright 2019 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "[http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0)\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    }
  ]
}